CINXE.COM

<!DOCTYPE html> <html lang="en" dir="ltr"> <head>  <script async src="https://www.googletagmanager.com/gtag/js?id=G-P63WKM1TM1"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-P63WKM1TM1'); </script>  <script type="text/javascript" > (function(m,e,t,r,i,k,a){m[i]=m[i]||function(){(m[i].a=m[i].a||[]).push(arguments)}; m[i].l=1*new Date(); for (var j = 0; j < document.scripts.length; j++) {if (document.scripts[j].src === r) { return; }} k=e.createElement(t),a=e.getElementsByTagName(t)[0],k.async=1,k.src=r,a.parentNode.insertBefore(k,a)}) (window, document, "script", "https://mc.yandex.ru/metrika/tag.js", "ym"); ym(55165297, "init", { clickmap:false, trackLinks:true, accurateTrackBounce:true, webvisor:false }); </script> <noscript><div><img src="https://mc.yandex.ru/watch/55165297" style="position:absolute; left:-9999px;" alt="" /></div></noscript>    <title>Search results for: supervised clustering</title> <meta name="description" content="Search results for: supervised clustering"> <meta name="keywords" content="supervised clustering"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" type="button" data-toggle="collapse" data-target="#navbarMenu" aria-controls="navbarMenu" aria-expanded="false" aria-label="Toggle navigation"> <span class="navbar-toggler-icon"></span> </button> <div class="w-100"> <div class="d-none d-lg-flex flex-row-reverse"> <form method="get" action="https://waset.org/search" class="form-inline my-2 my-lg-0"> <input class="form-control mr-sm-2" type="search" placeholder="Search Conferences" value="supervised clustering" name="q" aria-label="Search"> <button class="btn btn-light my-2 my-sm-0" type="submit"><i class="fas fa-search"></i></button> </form> </div> <div class="collapse navbar-collapse mt-1" id="navbarMenu"> <ul class="navbar-nav ml-auto align-items-center" id="mainNavMenu"> <li class="nav-item"> <a class="nav-link" href="https://waset.org/conferences" title="Conferences in 2024/2025/2026">Conferences</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/disciplines" title="Disciplines">Disciplines</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/committees" rel="nofollow">Committees</a> </li> <li class="nav-item dropdown"> <a class="nav-link dropdown-toggle" href="#" id="navbarDropdownPublications" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false"> Publications </a> <div class="dropdown-menu" aria-labelledby="navbarDropdownPublications"> <a class="dropdown-item" href="https://publications.waset.org/abstracts">Abstracts</a> <a class="dropdown-item" href="https://publications.waset.org">Periodicals</a> <a class="dropdown-item" href="https://publications.waset.org/archive">Archive</a> </div> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/page/support" title="Support">Support</a> </li> </ul> </div> </div> </nav> </div> </header> <main> <div class="container mt-4"> <div class="row"> <div class="col-md-9 mx-auto"> <form method="get" action="https://publications.waset.org/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="supervised clustering"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 565</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: supervised clustering</h1> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">565</span> Grid-based Supervised Clustering - GBSC</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Pornpimol%20Bungkomkhun">Pornpimol Bungkomkhun</a>, <a href="https://publications.waset.org/search?q=Surapong%20Auwatanamongkol"> Surapong Auwatanamongkol</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a supervised clustering algorithm, namely Grid-Based Supervised Clustering (GBSC), which is able to identify clusters of any shapes and sizes without presuming any canonical form for data distribution. The GBSC needs no prespecified number of clusters, is insensitive to the order of the input data objects, and is capable of handling outliers. Built on the combination of grid-based clustering and density-based clustering, under the assistance of the downward closure property of density used in bottom-up subspace clustering, the GBSC can notably reduce its search space to avoid the memory confinement situation during its execution. On two-dimension synthetic datasets, the GBSC can identify clusters with different shapes and sizes correctly. The GBSC also outperforms other five supervised clustering algorithms when the experiments are performed on some UCI datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=supervised%20clustering" title="supervised clustering">supervised clustering</a>, <a href="https://publications.waset.org/search?q=grid-based%20clustering" title=" grid-based clustering"> grid-based clustering</a>, <a href="https://publications.waset.org/search?q=subspace%20clustering" title="subspace clustering">subspace clustering</a> </p> <a href="https://publications.waset.org/11520/grid-based-supervised-clustering-gbsc" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/11520/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/11520/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/11520/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/11520/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/11520/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/11520/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/11520/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/11520/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/11520/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/11520/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/11520.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1610</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">564</span> Double Clustering as an Unsupervised Approach for Order Picking of Distributed Warehouses</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Hsin-Yi%20Huang">Hsin-Yi Huang</a>, <a href="https://publications.waset.org/search?q=Ming-Sheng%20Liu"> Ming-Sheng Liu</a>, <a href="https://publications.waset.org/search?q=Jiun-Yan%20Shiau"> Jiun-Yan Shiau</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Planning the order picking lists for warehouses to achieve some operational performances is a significant challenge when the costs associated with logistics are relatively high, and it is especially important in e-commerce era. Nowadays, many order planning techniques employ supervised machine learning algorithms. However, to define features for supervised machine learning algorithms is not a simple task. Against this background, we consider whether unsupervised algorithms can enhance the planning of order-picking lists. A double zone picking approach, which is based on using clustering algorithms twice, is developed. A simplified example is given to demonstrate the merit of our approach.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=order%20picking" title="order picking">order picking</a>, <a href="https://publications.waset.org/search?q=warehouse" title=" warehouse"> warehouse</a>, <a href="https://publications.waset.org/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/search?q=unsupervised%20learning" title=" unsupervised learning"> unsupervised learning</a> </p> <a href="https://publications.waset.org/10012251/double-clustering-as-an-unsupervised-approach-for-order-picking-of-distributed-warehouses" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10012251/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10012251/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10012251/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10012251/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10012251/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10012251/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10012251/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10012251/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10012251/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10012251/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10012251.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">520</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">563</span> A Constrained Clustering Algorithm for the Classification of Industrial Ores</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Luciano%20Nieddu">Luciano Nieddu</a>, <a href="https://publications.waset.org/search?q=Giuseppe%20Manfredi"> Giuseppe Manfredi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper a Pattern Recognition algorithm based on a constrained version of the k-means clustering algorithm will be presented. The proposed algorithm is a non parametric supervised statistical pattern recognition algorithm, i.e. it works under very mild assumptions on the dataset. The performance of the algorithm will be tested, togheter with a feature extraction technique that captures the information on the closed two-dimensional contour of an image, on images of industrial mineral ores. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=K-means" title="K-means">K-means</a>, <a href="https://publications.waset.org/search?q=Industrial%20ores%20classification" title=" Industrial ores classification"> Industrial ores classification</a>, <a href="https://publications.waset.org/search?q=Invariant%20Features" title=" Invariant Features"> Invariant Features</a>, <a href="https://publications.waset.org/search?q=Supervised%20Classification." title="Supervised Classification.">Supervised Classification.</a> </p> <a href="https://publications.waset.org/15142/a-constrained-clustering-algorithm-for-the-classification-of-industrial-ores" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/15142/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/15142/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/15142/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/15142/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/15142/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/15142/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/15142/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/15142/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/15142/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/15142/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/15142.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1381</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">562</span> Multi-Agent Systems for Intelligent Clustering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Jung-Eun%20Park">Jung-Eun Park</a>, <a href="https://publications.waset.org/search?q=Kyung-Whan%20Oh"> Kyung-Whan Oh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Intelligent systems are required in order to quickly and accurately analyze enormous quantities of data in the Internet environment. In intelligent systems, information extracting processes can be divided into supervised learning and unsupervised learning. This paper investigates intelligent clustering by unsupervised learning. Intelligent clustering is the clustering system which determines the clustering model for data analysis and evaluates results by itself. This system can make a clustering model more rapidly, objectively and accurately than an analyzer. The methodology for the automatic clustering intelligent system is a multi-agent system that comprises a clustering agent and a cluster performance evaluation agent. An agent exchanges information about clusters with another agent and the system determines the optimal cluster number through this information. Experiments using data sets in the UCI Machine Repository are performed in order to prove the validity of the system.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Intelligent%20Clustering" title="Intelligent Clustering">Intelligent Clustering</a>, <a href="https://publications.waset.org/search?q=Multi-Agent%20System" title=" Multi-Agent System"> Multi-Agent System</a>, <a href="https://publications.waset.org/search?q=PCA" title=" PCA"> PCA</a>, <a href="https://publications.waset.org/search?q=SOM" title="SOM">SOM</a>, <a href="https://publications.waset.org/search?q=VC%28Variance%20Criterion%29" title=" VC(Variance Criterion)"> VC(Variance Criterion)</a> </p> <a href="https://publications.waset.org/11193/multi-agent-systems-for-intelligent-clustering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/11193/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/11193/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/11193/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/11193/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/11193/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/11193/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/11193/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/11193/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/11193/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/11193/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/11193.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1727</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">561</span> Illumination Invariant Face Recognition using Supervised and Unsupervised Learning Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Shashank%20N.%20Mathur">Shashank N. Mathur</a>, <a href="https://publications.waset.org/search?q=Anil%20K.%20Ahlawat"> Anil K. Ahlawat</a>, <a href="https://publications.waset.org/search?q=Virendra%20P.%20Vishwakarma"> Virendra P. Vishwakarma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a comparative study of application of supervised and unsupervised learning algorithms on illumination invariant face recognition has been carried out. The supervised learning has been carried out with the help of using a bi-layered artificial neural network having one input, two hidden and one output layer. The gradient descent with momentum and adaptive learning rate back propagation learning algorithm has been used to implement the supervised learning in a way that both the inputs and corresponding outputs are provided at the time of training the network, thus here is an inherent clustering and optimized learning of weights which provide us with efficient results.. The unsupervised learning has been implemented with the help of a modified Counterpropagation network. The Counterpropagation network involves the process of clustering followed by application of Outstar rule to obtain the recognized face. The face recognition system has been developed for recognizing faces which have varying illumination intensities, where the database images vary in lighting with respect to angle of illumination with horizontal and vertical planes. The supervised and unsupervised learning algorithms have been implemented and have been tested exhaustively, with and without application of histogram equalization to get efficient results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Artificial%20Neural%20Networks" title="Artificial Neural Networks">Artificial Neural Networks</a>, <a href="https://publications.waset.org/search?q=back%20propagation" title=" back propagation"> back propagation</a>, <a href="https://publications.waset.org/search?q=Counterpropagation%20networks" title=" Counterpropagation networks"> Counterpropagation networks</a>, <a href="https://publications.waset.org/search?q=face%20recognition" title=" face recognition"> face recognition</a>, <a href="https://publications.waset.org/search?q=learning%20algorithms." title=" learning algorithms."> learning algorithms.</a> </p> <a href="https://publications.waset.org/11830/illumination-invariant-face-recognition-using-supervised-and-unsupervised-learning-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/11830/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/11830/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/11830/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/11830/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/11830/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/11830/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/11830/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/11830/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/11830/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/11830/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/11830.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1686</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">560</span> Fuzzy Types Clustering for Microarray Data </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Seo%20Young%20Kim">Seo Young Kim</a>, <a href="https://publications.waset.org/search?q=Tai%20Myong%20Choi"> Tai Myong Choi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main goal of microarray experiments is to quantify the expression of every object on a slide as precisely as possible, with a further goal of clustering the objects. Recently, many studies have discussed clustering issues involving similar patterns of gene expression. This paper presents an application of fuzzy-type methods for clustering DNA microarray data that can be applied to typical comparisons. Clustering and analyses were performed on microarray and simulated data. The results show that fuzzy-possibility c-means clustering substantially improves the findings obtained by others. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Clustering" title="Clustering">Clustering</a>, <a href="https://publications.waset.org/search?q=microarray%20data" title=" microarray data"> microarray data</a>, <a href="https://publications.waset.org/search?q=Fuzzy-type%20clustering" title=" Fuzzy-type clustering"> Fuzzy-type clustering</a>, <a href="https://publications.waset.org/search?q=Validation" title=" Validation"> Validation</a> </p> <a href="https://publications.waset.org/4776/fuzzy-types-clustering-for-microarray-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/4776/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/4776/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/4776/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/4776/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/4776/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/4776/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/4776/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/4776/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/4776/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/4776/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/4776.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1521</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">559</span> Human Digital Twin for Personal Conversation Automation Using Supervised Machine Learning Approaches</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Aya%20Salama">Aya Salama</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Digital Twin has emerged as a compelling research area, capturing the attention of scholars over the past decade. It finds applications across diverse fields, including smart manufacturing and healthcare, offering significant time and cost savings. Notably, it often intersects with other cutting-edge technologies such as Data Mining, Artificial Intelligence, and Machine Learning. However, the concept of a Human Digital Twin (HDT) is still in its infancy and requires further demonstration of its practicality. HDT takes the notion of Digital Twin a step further by extending it to living entities, notably humans, who are vastly different from inanimate physical objects. The primary objective of this research was to create an HDT capable of automating real-time human responses by simulating human behavior. To achieve this, the study delved into various areas, including clustering, supervised classification, topic extraction, and sentiment analysis. The paper successfully demonstrated the feasibility of HDT for generating personalized responses in social messaging applications. Notably, the proposed approach achieved an overall accuracy of 63%, a highly promising result that could pave the way for further exploration of the HDT concept. The methodology employed Random Forest for clustering the question database and matching new questions, while K-nearest neighbor was utilized for sentiment analysis.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Human%20Digital%20twin" title="Human Digital twin">Human Digital twin</a>, <a href="https://publications.waset.org/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a>, <a href="https://publications.waset.org/search?q=topic%0D%0Aextraction" title=" topic extraction"> topic extraction</a>, <a href="https://publications.waset.org/search?q=supervised%20machine%20learning" title=" supervised machine learning"> supervised machine learning</a>, <a href="https://publications.waset.org/search?q=unsupervised%20machine%0D%0Alearning" title=" unsupervised machine learning"> unsupervised machine learning</a>, <a href="https://publications.waset.org/search?q=classification%20and%20clustering." title=" classification and clustering."> classification and clustering.</a> </p> <a href="https://publications.waset.org/10013329/human-digital-twin-for-personal-conversation-automation-using-supervised-machine-learning-approaches" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10013329/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10013329/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10013329/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10013329/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10013329/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10013329/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10013329/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10013329/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10013329/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10013329/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10013329.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">188</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">558</span> Granulation using Clustering and Rough Set Theory and its Tree Representation </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Girish%20Kumar%20Singh">Girish Kumar Singh</a>, <a href="https://publications.waset.org/search?q=Sonajharia%20Minz"> Sonajharia Minz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Granular computing deals with representation of information in the form of some aggregates and related methods for transformation and analysis for problem solving. A granulation scheme based on clustering and Rough Set Theory is presented with focus on structured conceptualization of information has been presented in this paper. Experiments for the proposed method on four labeled data exhibit good result with reference to classification problem. The proposed granulation technique is semi-supervised imbibing global as well as local information granulation. To represent the results of the attribute oriented granulation a tree structure is proposed in this paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Granular%20computing" title="Granular computing">Granular computing</a>, <a href="https://publications.waset.org/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/search?q=Rough%20sets" title=" Rough sets"> Rough sets</a>, <a href="https://publications.waset.org/search?q=datamining." title=" datamining."> datamining.</a> </p> <a href="https://publications.waset.org/14084/granulation-using-clustering-and-rough-set-theory-and-its-tree-representation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/14084/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/14084/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/14084/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/14084/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/14084/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/14084/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/14084/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/14084/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/14084/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/14084/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/14084.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1719</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">557</span> A Optimal Subclass Detection Method for Credit Scoring</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Luciano%20Nieddu">Luciano Nieddu</a>, <a href="https://publications.waset.org/search?q=Giuseppe%20Manfredi"> Giuseppe Manfredi</a>, <a href="https://publications.waset.org/search?q=Salvatore%20D%27Acunto">Salvatore D'Acunto</a>, <a href="https://publications.waset.org/search?q=Katia%20La%20Regina"> Katia La Regina</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In this paper a non-parametric statistical pattern recognition algorithm for the problem of credit scoring will be presented. The proposed algorithm is based on a clustering k- means algorithm and allows for the determination of subclasses of homogenous elements in the data. The algorithm will be tested on two benchmark datasets and its performance compared with other well known pattern recognition algorithm for credit scoring.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Constrained%20clustering" title="Constrained clustering">Constrained clustering</a>, <a href="https://publications.waset.org/search?q=Credit%20scoring" title=" Credit scoring"> Credit scoring</a>, <a href="https://publications.waset.org/search?q=Statistical%20pattern%20recognition" title=" Statistical pattern recognition"> Statistical pattern recognition</a>, <a href="https://publications.waset.org/search?q=Supervised%20classification." title=" Supervised classification."> Supervised classification.</a> </p> <a href="https://publications.waset.org/51/a-optimal-subclass-detection-method-for-credit-scoring" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/51/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/51/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/51/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/51/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/51/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/51/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/51/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/51/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/51/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/51/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/51.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2049</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">556</span> Similarity Measures and Weighted Fuzzy C-Mean Clustering Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Bainian%20Li">Bainian Li</a>, <a href="https://publications.waset.org/search?q=Kongsheng%20Zhang"> Kongsheng Zhang</a>, <a href="https://publications.waset.org/search?q=Jian%20Xu"> Jian Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In this paper we study the fuzzy c-mean clustering algorithm combined with principal components method. Demonstratively analysis indicate that the new clustering method is well rather than some clustering algorithms. We also consider the validity of clustering method.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=FCM%20algorithm" title="FCM algorithm">FCM algorithm</a>, <a href="https://publications.waset.org/search?q=Principal%20Components%20Analysis" title=" Principal Components Analysis"> Principal Components Analysis</a>, <a href="https://publications.waset.org/search?q=Clustervalidity" title=" Clustervalidity"> Clustervalidity</a> </p> <a href="https://publications.waset.org/3579/similarity-measures-and-weighted-fuzzy-c-mean-clustering-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/3579/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/3579/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/3579/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/3579/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/3579/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/3579/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/3579/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/3579/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/3579/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/3579/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/3579.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1724</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">555</span> Exponential Particle Swarm Optimization Approach for Improving Data Clustering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Neveen%20I.%20Ghali">Neveen I. Ghali</a>, <a href="https://publications.waset.org/search?q=Nahed%20El-Dessouki"> Nahed El-Dessouki</a>, <a href="https://publications.waset.org/search?q=Mervat%20A.%20N."> Mervat A. N.</a>, <a href="https://publications.waset.org/search?q=Lamiaa%20Bakrawi"> Lamiaa Bakrawi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we use exponential particle swarm optimization (EPSO) to cluster data. Then we compare between (EPSO) clustering algorithm which depends on exponential variation for the inertia weight and particle swarm optimization (PSO) clustering algorithm which depends on linear inertia weight. This comparison is evaluated on five data sets. The experimental results show that EPSO clustering algorithm increases the possibility to find the optimal positions as it decrease the number of failure. Also show that (EPSO) clustering algorithm has a smaller quantization error than (PSO) clustering algorithm, i.e. (EPSO) clustering algorithm more accurate than (PSO) clustering algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Particle%20swarm%20optimization" title="Particle swarm optimization">Particle swarm optimization</a>, <a href="https://publications.waset.org/search?q=data%20clustering" title=" data clustering"> data clustering</a>, <a href="https://publications.waset.org/search?q=exponential%20PSO." title="exponential PSO.">exponential PSO.</a> </p> <a href="https://publications.waset.org/8531/exponential-particle-swarm-optimization-approach-for-improving-data-clustering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/8531/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/8531/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/8531/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/8531/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/8531/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/8531/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/8531/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/8531/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/8531/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/8531/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/8531.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1690</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">554</span> A Comparison of Fuzzy Clustering Algorithms to Cluster Web Messages</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Sara%20El%20Manar%20El%20Bouanani">Sara El Manar El Bouanani</a>, <a href="https://publications.waset.org/search?q=Ismail%20Kassou"> Ismail Kassou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Our objective in this paper is to propose an approach capable of clustering web messages. The clustering is carried out by assigning, with a certain probability, texts written by the same web user to the same cluster based on Stylometric features and using fuzzy clustering algorithms. Focus in the present work is on comparing the most popular algorithms in fuzzy clustering theory namely, Fuzzy C-means, Possibilistic C-means and Fuzzy Possibilistic C-Means.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Authorship%20detection" title="Authorship detection">Authorship detection</a>, <a href="https://publications.waset.org/search?q=fuzzy%20clustering" title=" fuzzy clustering"> fuzzy clustering</a>, <a href="https://publications.waset.org/search?q=profiling" title=" profiling"> profiling</a>, <a href="https://publications.waset.org/search?q=stylometric%20features." title=" stylometric features."> stylometric features.</a> </p> <a href="https://publications.waset.org/16551/a-comparison-of-fuzzy-clustering-algorithms-to-cluster-web-messages" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/16551/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/16551/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/16551/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/16551/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/16551/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/16551/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/16551/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/16551/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/16551/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/16551/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/16551.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2052</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">553</span> Rapid Study on Feature Extraction and Classification Models in Healthcare Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=S.%20Sowmyayani">S. Sowmyayani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>The advancement of computer-aided design helps the medical force and security force. Some applications include biometric recognition, elderly fall detection, face recognition, cancer recognition, tumor recognition, etc. This paper deals with different machine learning algorithms that are more generically used for any health care system. The most focused problems are classification and regression. With the rise of big data, machine learning has become particularly important for solving problems. Machine learning uses two types of techniques: supervised learning and unsupervised learning. The former trains a model on known input and output data and predicts future outputs. Classification and regression are supervised learning techniques. Unsupervised learning finds hidden patterns in input data. Clustering is one such unsupervised learning technique. The above-mentioned models are discussed briefly in this paper.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Supervised%20learning" title="Supervised learning">Supervised learning</a>, <a href="https://publications.waset.org/search?q=unsupervised%20learning" title=" unsupervised learning"> unsupervised learning</a>, <a href="https://publications.waset.org/search?q=regression" title=" regression"> regression</a>, <a href="https://publications.waset.org/search?q=neural%20network." title=" neural network."> neural network.</a> </p> <a href="https://publications.waset.org/10012858/rapid-study-on-feature-extraction-and-classification-models-in-healthcare-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10012858/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10012858/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10012858/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10012858/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10012858/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10012858/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10012858/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10012858/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10012858/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10012858/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10012858.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">346</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">552</span> Analysis of Diverse Clustering Tools in Data Mining</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=S.%20Sarumathi">S. Sarumathi</a>, <a href="https://publications.waset.org/search?q=N.%20Shanthi"> N. Shanthi</a>, <a href="https://publications.waset.org/search?q=M.%20Sharmila"> M. Sharmila</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Clustering in data mining is an unsupervised learning technique of aggregating the data objects into meaningful groups such that the intra cluster similarity of objects are maximized and inter cluster similarity of objects are minimized. Over the past decades several clustering tools were emerged in which clustering algorithms are inbuilt and are easier to use and extract the expected results. Data mining mainly deals with the huge databases that inflicts on cluster analysis and additional rigorous computational constraints. These challenges pave the way for the emergence of powerful expansive data mining clustering softwares. In this survey, a variety of clustering tools used in data mining are elucidated along with the pros and cons of each software.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Cluster%20Analysis" title="Cluster Analysis">Cluster Analysis</a>, <a href="https://publications.waset.org/search?q=Clustering%20Algorithms" title=" Clustering Algorithms"> Clustering Algorithms</a>, <a href="https://publications.waset.org/search?q=Clustering%20Techniques" title=" Clustering Techniques"> Clustering Techniques</a>, <a href="https://publications.waset.org/search?q=Association" title=" Association"> Association</a>, <a href="https://publications.waset.org/search?q=Visualization." title=" Visualization."> Visualization.</a> </p> <a href="https://publications.waset.org/9997173/analysis-of-diverse-clustering-tools-in-data-mining" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9997173/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9997173/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9997173/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9997173/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9997173/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9997173/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9997173/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9997173/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9997173/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9997173/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9997173.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2201</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">551</span> Hierarchical Clustering Algorithms in Data Mining</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Z.%20Abdullah">Z. Abdullah</a>, <a href="https://publications.waset.org/search?q=A.%20R.%20Hamdan"> A. R. Hamdan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Clustering is a process of grouping objects and data into groups of clusters to ensure that data objects from the same cluster are identical to each other. Clustering algorithms in one of the area in data mining and it can be classified into partition, hierarchical, density based and grid based. Therefore, in this paper we do survey and review four major hierarchical clustering algorithms called CURE, ROCK, CHAMELEON and BIRCH. The obtained state of the art of these algorithms will help in eliminating the current problems as well as deriving more robust and scalable algorithms for clustering. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Clustering" title="Clustering">Clustering</a>, <a href="https://publications.waset.org/search?q=method" title=" method"> method</a>, <a href="https://publications.waset.org/search?q=algorithm" title=" algorithm"> algorithm</a>, <a href="https://publications.waset.org/search?q=hierarchical" title=" hierarchical"> hierarchical</a>, <a href="https://publications.waset.org/search?q=survey." title=" survey."> survey.</a> </p> <a href="https://publications.waset.org/10002625/hierarchical-clustering-algorithms-in-data-mining" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10002625/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10002625/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10002625/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10002625/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10002625/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10002625/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10002625/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10002625/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10002625/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10002625/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10002625.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">3376</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">550</span> A Survey: Clustering Ensembles Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Reza%20Ghaemi">Reza Ghaemi </a>, <a href="https://publications.waset.org/search?q=Md.%20Nasir%20Sulaiman"> Md. Nasir Sulaiman </a>, <a href="https://publications.waset.org/search?q=Hamidah%20Ibrahim"> Hamidah Ibrahim </a>, <a href="https://publications.waset.org/search?q=Norwati%20Mustapha"> Norwati Mustapha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The clustering ensembles combine multiple partitions generated by different clustering algorithms into a single clustering solution. Clustering ensembles have emerged as a prominent method for improving robustness, stability and accuracy of unsupervised classification solutions. So far, many contributions have been done to find consensus clustering. One of the major problems in clustering ensembles is the consensus function. In this paper, firstly, we introduce clustering ensembles, representation of multiple partitions, its challenges and present taxonomy of combination algorithms. Secondly, we describe consensus functions in clustering ensembles including Hypergraph partitioning, Voting approach, Mutual information, Co-association based functions and Finite mixture model, and next explain their advantages, disadvantages and computational complexity. Finally, we compare the characteristics of clustering ensembles algorithms such as computational complexity, robustness, simplicity and accuracy on different datasets in previous techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Clustering%20Ensembles" title="Clustering Ensembles">Clustering Ensembles</a>, <a href="https://publications.waset.org/search?q=Combinational%20Algorithm" title=" Combinational Algorithm"> Combinational Algorithm</a>, <a href="https://publications.waset.org/search?q=Consensus%20Function" title="Consensus Function">Consensus Function</a>, <a href="https://publications.waset.org/search?q=Unsupervised%20Classification." title=" Unsupervised Classification."> Unsupervised Classification.</a> </p> <a href="https://publications.waset.org/898/a-survey-clustering-ensembles-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/898/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/898/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/898/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/898/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/898/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/898/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/898/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/898/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/898/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/898/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/898.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">3449</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">549</span> Ontology-based Concept Weighting for Text Documents</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Hmway%20Hmway%20Tar">Hmway Hmway Tar</a>, <a href="https://publications.waset.org/search?q=Thi%20Thi%20Soe%20Nyaunt"> Thi Thi Soe Nyaunt</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Documents clustering become an essential technology with the popularity of the Internet. That also means that fast and high-quality document clustering technique play core topics. Text clustering or shortly clustering is about discovering semantically related groups in an unstructured collection of documents. Clustering has been very popular for a long time because it provides unique ways of digesting and generalizing large amounts of information. One of the issues of clustering is to extract proper feature (concept) of a problem domain. The existing clustering technology mainly focuses on term weight calculation. To achieve more accurate document clustering, more informative features including concept weight are important. Feature Selection is important for clustering process because some of the irrelevant or redundant feature may misguide the clustering results. To counteract this issue, the proposed system presents the concept weight for text clustering system developed based on a k-means algorithm in accordance with the principles of ontology so that the important of words of a cluster can be identified by the weight values. To a certain extent, it has resolved the semantic problem in specific areas. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Clustering" title="Clustering">Clustering</a>, <a href="https://publications.waset.org/search?q=Concept%20Weight" title=" Concept Weight"> Concept Weight</a>, <a href="https://publications.waset.org/search?q=Document%20clustering" title=" Document clustering"> Document clustering</a>, <a href="https://publications.waset.org/search?q=Feature%20Selection" title="Feature Selection">Feature Selection</a>, <a href="https://publications.waset.org/search?q=Ontology" title=" Ontology"> Ontology</a> </p> <a href="https://publications.waset.org/631/ontology-based-concept-weighting-for-text-documents" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/631/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/631/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/631/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/631/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/631/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/631/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/631/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/631/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/631/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/631/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/631.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2405</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">548</span> Journey on Image Clustering Based on Color Composition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Achmad%20Nizar%20Hidayanto">Achmad Nizar Hidayanto</a>, <a href="https://publications.waset.org/search?q=Elisabeth%20Martha%20Koeanan"> Elisabeth Martha Koeanan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image clustering is a process of grouping images based on their similarity. The image clustering usually uses the color component, texture, edge, shape, or mixture of two components, etc. This research aims to explore image clustering using color composition. In order to complete this image clustering, three main components should be considered, which are color space, image representation (feature extraction), and clustering method itself. We aim to explore which composition of these factors will produce the best clustering results by combining various techniques from the three components. The color spaces use RGB, HSV, and L*a*b* method. The image representations use Histogram and Gaussian Mixture Model (GMM), whereas the clustering methods use KMeans and Agglomerative Hierarchical Clustering algorithm. The results of the experiment show that GMM representation is better combined with RGB and L*a*b* color space, whereas Histogram is better combined with HSV. The experiments also show that K-Means is better than Agglomerative Hierarchical for images clustering. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Image%20clustering" title="Image clustering">Image clustering</a>, <a href="https://publications.waset.org/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/search?q=RGB" title=" RGB"> RGB</a>, <a href="https://publications.waset.org/search?q=HSV" title=" HSV"> HSV</a>, <a href="https://publications.waset.org/search?q=L%2Aa%2Ab%2A" title="L*a*b*">L*a*b*</a>, <a href="https://publications.waset.org/search?q=Gaussian%20Mixture%20Model%20%28GMM%29" title=" Gaussian Mixture Model (GMM)"> Gaussian Mixture Model (GMM)</a>, <a href="https://publications.waset.org/search?q=histogram" title=" histogram"> histogram</a>, <a href="https://publications.waset.org/search?q=Agglomerative%20Hierarchical%20Clustering%20%28AHC%29" title="Agglomerative Hierarchical Clustering (AHC)">Agglomerative Hierarchical Clustering (AHC)</a>, <a href="https://publications.waset.org/search?q=K-Means" title=" K-Means"> K-Means</a>, <a href="https://publications.waset.org/search?q=Expectation-Maximization%20%28EM%29." title="Expectation-Maximization (EM).">Expectation-Maximization (EM).</a> </p> <a href="https://publications.waset.org/11825/journey-on-image-clustering-based-on-color-composition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/11825/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/11825/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/11825/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/11825/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/11825/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/11825/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/11825/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/11825/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/11825/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/11825/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/11825.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2206</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">547</span> Sample-Weighted Fuzzy Clustering with Regularizations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Miin-Shen%20Yang">Miin-Shen Yang</a>, <a href="https://publications.waset.org/search?q=Yee-Shan%20Pan"> Yee-Shan Pan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Although there have been many researches in cluster analysis to consider on feature weights, little effort is made on sample weights. Recently, Yu et al. (2011) considered a probability distribution over a data set to represent its sample weights and then proposed sample-weighted clustering algorithms. In this paper, we give a sample-weighted version of generalized fuzzy clustering regularization (GFCR), called the sample-weighted GFCR (SW-GFCR). Some experiments are considered. These experimental results and comparisons demonstrate that the proposed SW-GFCR is more effective than the most clustering algorithms.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Clustering%3B%20fuzzy%20c-means" title="Clustering; fuzzy c-means">Clustering; fuzzy c-means</a>, <a href="https://publications.waset.org/search?q=fuzzy%20clustering" title=" fuzzy clustering"> fuzzy clustering</a>, <a href="https://publications.waset.org/search?q=sample%0D%0Aweights" title=" sample weights"> sample weights</a>, <a href="https://publications.waset.org/search?q=regularization." title=" regularization."> regularization.</a> </p> <a href="https://publications.waset.org/16400/sample-weighted-fuzzy-clustering-with-regularizations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/16400/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/16400/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/16400/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/16400/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/16400/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/16400/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/16400/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/16400/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/16400/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/16400/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/16400.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1765</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">546</span> Application of a New Hybrid Optimization Algorithm on Cluster Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=T.%20Niknam">T. Niknam</a>, <a href="https://publications.waset.org/search?q=M.%20Nayeripour"> M. Nayeripour</a>, <a href="https://publications.waset.org/search?q=B.Bahmani%20Firouzi"> B.Bahmani Firouzi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Clustering techniques have received attention in many areas including engineering, medicine, biology and data mining. The purpose of clustering is to group together data points, which are close to one another. The K-means algorithm is one of the most widely used techniques for clustering. However, K-means has two shortcomings: dependency on the initial state and convergence to local optima and global solutions of large problems cannot found with reasonable amount of computation effort. In order to overcome local optima problem lots of studies done in clustering. This paper is presented an efficient hybrid evolutionary optimization algorithm based on combining Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), called PSO-ACO, for optimally clustering N object into K clusters. The new PSO-ACO algorithm is tested on several data sets, and its performance is compared with those of ACO, PSO and K-means clustering. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for handing data clustering.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Ant%20Colony%20Optimization%20%28ACO%29" title="Ant Colony Optimization (ACO)">Ant Colony Optimization (ACO)</a>, <a href="https://publications.waset.org/search?q=Data%20clustering" title=" Data clustering"> Data clustering</a>, <a href="https://publications.waset.org/search?q=Hybrid%20evolutionary%20optimization%20algorithm" title=" Hybrid evolutionary optimization algorithm"> Hybrid evolutionary optimization algorithm</a>, <a href="https://publications.waset.org/search?q=K-means%20clustering" title=" K-means clustering"> K-means clustering</a>, <a href="https://publications.waset.org/search?q=Particle%20Swarm%20Optimization%20%28PSO%29." title=" Particle Swarm Optimization (PSO)."> Particle Swarm Optimization (PSO).</a> </p> <a href="https://publications.waset.org/10736/application-of-a-new-hybrid-optimization-algorithm-on-cluster-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10736/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10736/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10736/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10736/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10736/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10736/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10736/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10736/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10736/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10736/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10736.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2198</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">545</span> A Similarity Measure for Clustering and its Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Guadalupe%20J.%20Torres">Guadalupe J. Torres</a>, <a href="https://publications.waset.org/search?q=Ram%20B.%20Basnet"> Ram B. Basnet</a>, <a href="https://publications.waset.org/search?q=Andrew%20H.%20Sung"> Andrew H. Sung</a>, <a href="https://publications.waset.org/search?q=Srinivas%20Mukkamala"> Srinivas Mukkamala</a>, <a href="https://publications.waset.org/search?q=Bernardete%20M.%20Ribeiro"> Bernardete M. Ribeiro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces a measure of similarity between two clusterings of the same dataset produced by two different algorithms, or even the same algorithm (K-means, for instance, with different initializations usually produce different results in clustering the same dataset). We then apply the measure to calculate the similarity between pairs of clusterings, with special interest directed at comparing the similarity between various machine clusterings and human clustering of datasets. The similarity measure thus can be used to identify the best (in terms of most similar to human) clustering algorithm for a specific problem at hand. Experimental results pertaining to the text categorization problem of a Portuguese corpus (wherein a translation-into-English approach is used) are presented, as well as results on the well-known benchmark IRIS dataset. The significance and other potential applications of the proposed measure are discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Clustering%20Algorithms" title="Clustering Algorithms">Clustering Algorithms</a>, <a href="https://publications.waset.org/search?q=Clustering%20Applications" title=" Clustering Applications"> Clustering Applications</a>, <a href="https://publications.waset.org/search?q=Similarity%20Measures" title=" Similarity Measures"> Similarity Measures</a>, <a href="https://publications.waset.org/search?q=Text%20Clustering" title=" Text Clustering"> Text Clustering</a> </p> <a href="https://publications.waset.org/9316/a-similarity-measure-for-clustering-and-its-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9316/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9316/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9316/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9316/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9316/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9316/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9316/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9316/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9316/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9316/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9316.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1571</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">544</span> Clustering in WSN Based on Minimum Spanning Tree Using Divide and Conquer Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Uttam%20Vijay">Uttam Vijay</a>, <a href="https://publications.waset.org/search?q=Nitin%20Gupta"> Nitin Gupta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Due to heavy energy constraints in WSNs clustering is an efficient way to manage the energy in sensors. There are many methods already proposed in the area of clustering and research is still going on to make clustering more energy efficient. In our paper we are proposing a minimum spanning tree based clustering using divide and conquer approach. The MST based clustering was first proposed in 1970’s for large databases. Here we are taking divide and conquer approach and implementing it for wireless sensor networks with the constraints attached to the sensor networks. This Divide and conquer approach is implemented in a way that we don’t have to construct the whole MST before clustering but we just find the edge which will be the part of the MST to a corresponding graph and divide the graph in clusters there itself if that edge from the graph can be removed judging on certain constraints and hence saving lot of computation.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Algorithm" title="Algorithm">Algorithm</a>, <a href="https://publications.waset.org/search?q=Clustering" title=" Clustering"> Clustering</a>, <a href="https://publications.waset.org/search?q=Edge-Weighted%20Graph" title=" Edge-Weighted Graph"> Edge-Weighted Graph</a>, <a href="https://publications.waset.org/search?q=Weighted-LEACH." title=" Weighted-LEACH."> Weighted-LEACH.</a> </p> <a href="https://publications.waset.org/16354/clustering-in-wsn-based-on-minimum-spanning-tree-using-divide-and-conquer-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/16354/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/16354/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/16354/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/16354/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/16354/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/16354/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/16354/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/16354/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/16354/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/16354/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/16354.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2475</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">543</span> Minimal Spanning Tree based Fuzzy Clustering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=%C3%81gnes%20Vathy-Fogarassy">Ágnes Vathy-Fogarassy</a>, <a href="https://publications.waset.org/search?q=Bal%C3%A1zs%20Feil"> Balázs Feil</a>, <a href="https://publications.waset.org/search?q=J%C3%A1nos%20Abonyi"> János Abonyi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most of fuzzy clustering algorithms have some discrepancies, e.g. they are not able to detect clusters with convex shapes, the number of the clusters should be a priori known, they suffer from numerical problems, like sensitiveness to the initialization, etc. This paper studies the synergistic combination of the hierarchical and graph theoretic minimal spanning tree based clustering algorithm with the partitional Gath-Geva fuzzy clustering algorithm. The aim of this hybridization is to increase the robustness and consistency of the clustering results and to decrease the number of the heuristically defined parameters of these algorithms to decrease the influence of the user on the clustering results. For the analysis of the resulted fuzzy clusters a new fuzzy similarity measure based tool has been presented. The calculated similarities of the clusters can be used for the hierarchical clustering of the resulted fuzzy clusters, which information is useful for cluster merging and for the visualization of the clustering results. As the examples used for the illustration of the operation of the new algorithm will show, the proposed algorithm can detect clusters from data with arbitrary shape and does not suffer from the numerical problems of the classical Gath-Geva fuzzy clustering algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Clustering" title="Clustering">Clustering</a>, <a href="https://publications.waset.org/search?q=fuzzy%20clustering" title=" fuzzy clustering"> fuzzy clustering</a>, <a href="https://publications.waset.org/search?q=minimal%20spanning%20tree" title=" minimal spanning tree"> minimal spanning tree</a>, <a href="https://publications.waset.org/search?q=cluster%20validity" title="cluster validity">cluster validity</a>, <a href="https://publications.waset.org/search?q=fuzzy%20similarity." title=" fuzzy similarity."> fuzzy similarity.</a> </p> <a href="https://publications.waset.org/1824/minimal-spanning-tree-based-fuzzy-clustering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/1824/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/1824/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/1824/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/1824/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/1824/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/1824/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/1824/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/1824/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/1824/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/1824/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/1824.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2406</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">542</span> Using Data Clustering in Oral Medicine</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Fahad%20Shahbaz%20Khan">Fahad Shahbaz Khan</a>, <a href="https://publications.waset.org/search?q=Rao%20Muhammad%20Anwer"> Rao Muhammad Anwer</a>, <a href="https://publications.waset.org/search?q=Olof%20Torgersson"> Olof Torgersson</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The vast amount of information hidden in huge databases has created tremendous interests in the field of data mining. This paper examines the possibility of using data clustering techniques in oral medicine to identify functional relationships between different attributes and classification of similar patient examinations. Commonly used data clustering algorithms have been reviewed and as a result several interesting results have been gathered. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Oral%20Medicine" title="Oral Medicine">Oral Medicine</a>, <a href="https://publications.waset.org/search?q=Cluto" title=" Cluto"> Cluto</a>, <a href="https://publications.waset.org/search?q=Data%20Clustering" title=" Data Clustering"> Data Clustering</a>, <a href="https://publications.waset.org/search?q=Data%20Mining." title=" Data Mining."> Data Mining.</a> </p> <a href="https://publications.waset.org/1717/using-data-clustering-in-oral-medicine" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/1717/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/1717/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/1717/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/1717/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/1717/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/1717/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/1717/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/1717/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/1717/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/1717/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/1717.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1977</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">541</span> A Genetic Algorithm for Clustering on Image Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Qin%20Ding">Qin Ding</a>, <a href="https://publications.waset.org/search?q=Jim%20Gasvoda"> Jim Gasvoda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Clustering is the process of subdividing an input data set into a desired number of subgroups so that members of the same subgroup are similar and members of different subgroups have diverse properties. Many heuristic algorithms have been applied to the clustering problem, which is known to be NP Hard. Genetic algorithms have been used in a wide variety of fields to perform clustering, however, the technique normally has a long running time in terms of input set size. This paper proposes an efficient genetic algorithm for clustering on very large data sets, especially on image data sets. The genetic algorithm uses the most time efficient techniques along with preprocessing of the input data set. We test our algorithm on both artificial and real image data sets, both of which are of large size. The experimental results show that our algorithm outperforms the k-means algorithm in terms of running time as well as the quality of the clustering.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Clustering" title="Clustering">Clustering</a>, <a href="https://publications.waset.org/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/search?q=image%20data." title=" image data."> image data.</a> </p> <a href="https://publications.waset.org/13536/a-genetic-algorithm-for-clustering-on-image-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/13536/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/13536/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/13536/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/13536/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/13536/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/13536/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/13536/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/13536/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/13536/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/13536/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/13536.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2053</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">540</span> A Modified Fuzzy C-Means Algorithm for Natural Data Exploration</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Binu%20Thomas">Binu Thomas</a>, <a href="https://publications.waset.org/search?q=Raju%20G."> Raju G.</a>, <a href="https://publications.waset.org/search?q=Sonam%20Wangmo"> Sonam Wangmo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Data mining, Fuzzy clustering algorithms have demonstrated advantage over crisp clustering algorithms in dealing with the challenges posed by large collections of vague and uncertain natural data. This paper reviews concept of fuzzy logic and fuzzy clustering. The classical fuzzy c-means algorithm is presented and its limitations are highlighted. Based on the study of the fuzzy c-means algorithm and its extensions, we propose a modification to the cmeans algorithm to overcome the limitations of it in calculating the new cluster centers and in finding the membership values with natural data. The efficiency of the new modified method is demonstrated on real data collected for Bhutan-s Gross National Happiness (GNH) program. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Adaptive%20fuzzy%20clustering" title="Adaptive fuzzy clustering">Adaptive fuzzy clustering</a>, <a href="https://publications.waset.org/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</a>, <a href="https://publications.waset.org/search?q=fuzzy%20clustering" title=" fuzzy clustering"> fuzzy clustering</a>, <a href="https://publications.waset.org/search?q=c-means." title=" c-means."> c-means.</a> </p> <a href="https://publications.waset.org/11192/a-modified-fuzzy-c-means-algorithm-for-natural-data-exploration" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/11192/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/11192/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/11192/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/11192/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/11192/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/11192/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/11192/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/11192/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/11192/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/11192/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/11192.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1990</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">539</span> A Text Clustering System based on k-means Type Subspace Clustering and Ontology</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Liping%20Jing">Liping Jing</a>, <a href="https://publications.waset.org/search?q=Michael%20K.%20Ng"> Michael K. Ng</a>, <a href="https://publications.waset.org/search?q=Xinhua%20Yang"> Xinhua Yang</a>, <a href="https://publications.waset.org/search?q=Joshua%20Zhexue%20Huang"> Joshua Zhexue Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>This paper presents a text clustering system developed based on a k-means type subspace clustering algorithm to cluster large, high dimensional and sparse text data. In this algorithm, a new step is added in the k-means clustering process to automatically calculate the weights of keywords in each cluster so that the important words of a cluster can be identified by the weight values. For understanding and interpretation of clustering results, a few keywords that can best represent the semantic topic are extracted from each cluster. Two methods are used to extract the representative words. The candidate words are first selected according to their weights calculated by our new algorithm. Then, the candidates are fed to the WordNet to identify the set of noun words and consolidate the synonymy and hyponymy words. Experimental results have shown that the clustering algorithm is superior to the other subspace clustering algorithms, such as PROCLUS and HARP and kmeans type algorithm, e.g., Bisecting-KMeans. Furthermore, the word extraction method is effective in selection of the words to represent the topics of the clusters.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Subspace%20Clustering" title="Subspace Clustering">Subspace Clustering</a>, <a href="https://publications.waset.org/search?q=Text%20Mining" title=" Text Mining"> Text Mining</a>, <a href="https://publications.waset.org/search?q=Feature%20Weighting" title=" Feature Weighting"> Feature Weighting</a>, <a href="https://publications.waset.org/search?q=Cluster%20Interpretation" title=" Cluster Interpretation"> Cluster Interpretation</a>, <a href="https://publications.waset.org/search?q=Ontology" title=" Ontology"> Ontology</a> </p> <a href="https://publications.waset.org/2401/a-text-clustering-system-based-on-k-means-type-subspace-clustering-and-ontology" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/2401/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/2401/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/2401/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/2401/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/2401/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/2401/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/2401/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/2401/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/2401/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/2401/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/2401.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2462</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">538</span> ISC–Intelligent Subspace Clustering, A Density Based Clustering Approach for High Dimensional Dataset</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Sunita%20Jahirabadkar">Sunita Jahirabadkar</a>, <a href="https://publications.waset.org/search?q=Parag%20Kulkarni"> Parag Kulkarni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Many real-world data sets consist of a very high dimensional feature space. Most clustering techniques use the distance or similarity between objects as a measure to build clusters. But in high dimensional spaces, distances between points become relatively uniform. In such cases, density based approaches may give better results. Subspace Clustering algorithms automatically identify lower dimensional subspaces of the higher dimensional feature space in which clusters exist. In this paper, we propose a new clustering algorithm, ISC – Intelligent Subspace Clustering, which tries to overcome three major limitations of the existing state-of-art techniques. ISC determines the input parameter such as є – distance at various levels of Subspace Clustering which helps in finding meaningful clusters. The uniform parameters approach is not suitable for different kind of databases. ISC implements dynamic and adaptive determination of Meaningful clustering parameters based on hierarchical filtering approach. Third and most important feature of ISC is the ability of incremental learning and dynamic inclusion and exclusions of subspaces which lead to better cluster formation.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Density%20based%20clustering" title="Density based clustering">Density based clustering</a>, <a href="https://publications.waset.org/search?q=high%20dimensional%20data" title=" high dimensional data"> high dimensional data</a>, <a href="https://publications.waset.org/search?q=subspace%20clustering" title=" subspace clustering"> subspace clustering</a>, <a href="https://publications.waset.org/search?q=dynamic%20parameter%20setting." title=" dynamic parameter setting."> dynamic parameter setting.</a> </p> <a href="https://publications.waset.org/13091/isc-intelligent-subspace-clustering-a-density-based-clustering-approach-for-high-dimensional-dataset" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/13091/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/13091/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/13091/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/13091/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/13091/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/13091/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/13091/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/13091/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/13091/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/13091/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/13091.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2018</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">537</span> Energy Efficient Clustering Algorithm with Global and Local Re-clustering for Wireless Sensor Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Ashanie%20Guanathillake">Ashanie Guanathillake</a>, <a href="https://publications.waset.org/search?q=Kithsiri%20Samarasinghe"> Kithsiri Samarasinghe</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Wireless Sensor Networks consist of inexpensive, low power sensor nodes deployed to monitor the environment and collect data. Gathering information in an energy efficient manner is a critical aspect to prolong the network lifetime. Clustering  algorithms have an advantage of enhancing the network lifetime. Current clustering algorithms usually focus on global re-clustering and local re-clustering separately. This paper, proposed a combination of those two reclustering methods to reduce the energy consumption of the network. Furthermore, the proposed algorithm can apply to homogeneous as well as heterogeneous wireless sensor networks. In addition, the cluster head rotation happens, only when its energy drops below a dynamic threshold value computed by the algorithm. The simulation result shows that the proposed algorithm prolong the network lifetime compared to existing algorithms.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Energy%20efficient" title="Energy efficient">Energy efficient</a>, <a href="https://publications.waset.org/search?q=Global%20re-clustering" title=" Global re-clustering"> Global re-clustering</a>, <a href="https://publications.waset.org/search?q=Local%20re-clustering" title=" Local re-clustering"> Local re-clustering</a>, <a href="https://publications.waset.org/search?q=Wireless%20sensor%20networks." title=" Wireless sensor networks."> Wireless sensor networks.</a> </p> <a href="https://publications.waset.org/16258/energy-efficient-clustering-algorithm-with-global-and-local-re-clustering-for-wireless-sensor-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/16258/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/16258/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/16258/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/16258/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/16258/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/16258/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/16258/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/16258/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/16258/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/16258/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/16258.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2370</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">536</span> Observations about the Principal Components Analysis and Data Clustering Techniques in the Study of Medical Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Cristina%20G.%20Dasc%C3%A2lu">Cristina G. Dascâlu</a>, <a href="https://publications.waset.org/search?q=Corina%20Dima%20Cozma"> Corina Dima Cozma</a>, <a href="https://publications.waset.org/search?q=Elena%20Carmen%20Cotrutz"> Elena Carmen Cotrutz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The medical data statistical analysis often requires the using of some special techniques, because of the particularities of these data. The principal components analysis and the data clustering are two statistical methods for data mining very useful in the medical field, the first one as a method to decrease the number of studied parameters, and the second one as a method to analyze the connections between diagnosis and the data about the patient-s condition. In this paper we investigate the implications obtained from a specific data analysis technique: the data clustering preceded by a selection of the most relevant parameters, made using the principal components analysis. Our assumption was that, using the principal components analysis before data clustering - in order to select and to classify only the most relevant parameters – the accuracy of clustering is improved, but the practical results showed the opposite fact: the clustering accuracy decreases, with a percentage approximately equal with the percentage of information loss reported by the principal components analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Data%20clustering" title="Data clustering">Data clustering</a>, <a href="https://publications.waset.org/search?q=medical%20data" title=" medical data"> medical data</a>, <a href="https://publications.waset.org/search?q=principal%20components%0Aanalysis." title=" principal components analysis."> principal components analysis.</a> </p> <a href="https://publications.waset.org/15595/observations-about-the-principal-components-analysis-and-data-clustering-techniques-in-the-study-of-medical-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/15595/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/15595/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/15595/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/15595/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/15595/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/15595/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/15595/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/15595/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/15595/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/15595/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/15595.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1501</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/search?q=supervised%20clustering&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/search?q=supervised%20clustering&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/search?q=supervised%20clustering&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/search?q=supervised%20clustering&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/search?q=supervised%20clustering&page=6">6</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/search?q=supervised%20clustering&page=7">7</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/search?q=supervised%20clustering&page=8">8</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/search?q=supervised%20clustering&page=9">9</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/search?q=supervised%20clustering&page=10">10</a></li> <li class="page-item disabled"><span class="page-link">...</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/search?q=supervised%20clustering&page=18">18</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/search?q=supervised%20clustering&page=19">19</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/search?q=supervised%20clustering&page=2" rel="next">&rsaquo;</a></li> </ul> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); });*/ jQuery.get({ url: "https://publications.waset.org/xhr/user-menu", cache: false }).then(function(response){ jQuery('#mainNavMenu').append(response); }); }); </script> </body> </html>