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clustering</title> <meta name="description" content="Search results for: documents clustering"> <meta name="keywords" content="documents 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 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action="https://publications.waset.org/abstracts/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="documents 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> 1520</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: documents clustering</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1520</span> Using Closed Frequent Itemsets for Hierarchical Document Clustering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cheng-Jhe%20Lee">Cheng-Jhe Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Chiun-Chieh%20Hsu"> Chiun-Chieh Hsu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the rapid development of the Internet and the increased availability of digital documents, the excessive information on the Internet has led to information overflow problem. In order to solve these problems for effective information retrieval, document clustering in text mining becomes a popular research topic. Clustering is the unsupervised classification of data items into groups without the need of training data. Many conventional document clustering methods perform inefficiently for large document collections because they were originally designed for relational database. Therefore they are impractical in real-world document clustering and require special handling for high dimensionality and high volume. We propose the FIHC (Frequent Itemset-based Hierarchical Clustering) method, which is a hierarchical clustering method developed for document clustering, where the intuition of FIHC is that there exist some common words for each cluster. FIHC uses such words to cluster documents and builds hierarchical topic tree. In this paper, we combine FIHC algorithm with ontology to solve the semantic problem and mine the meaning behind the words in documents. Furthermore, we use the closed frequent itemsets instead of only use frequent itemsets, which increases efficiency and scalability. The experimental results show that our method is more accurate than those of well-known document clustering algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=FIHC" title="FIHC">FIHC</a>, <a href="https://publications.waset.org/abstracts/search?q=documents%20clustering" title=" documents clustering"> documents clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=ontology" title=" ontology"> ontology</a>, <a href="https://publications.waset.org/abstracts/search?q=closed%20frequent%20itemset" title=" closed frequent itemset"> closed frequent itemset</a> </p> <a href="https://publications.waset.org/abstracts/41381/using-closed-frequent-itemsets-for-hierarchical-document-clustering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41381.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">399</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1519</span> A Similarity Measure for Classification and Clustering in Image Based Medical and Text Based Banking Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20P.%20Sandesh">K. P. Sandesh</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20H.%20Suman"> M. H. Suman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Text processing plays an important role in information retrieval, data-mining, and web search. Measuring the similarity between the documents is an important operation in the text processing field. In this project, a new similarity measure is proposed. To compute the similarity between two documents with respect to a feature the proposed measure takes the following three cases into account: (1) The feature appears in both documents; (2) The feature appears in only one document and; (3) The feature appears in none of the documents. The proposed measure is extended to gauge the similarity between two sets of documents. The effectiveness of our measure is evaluated on several real-world data sets for text classification and clustering problems, especially in banking and health sectors. The results show that the performance obtained by the proposed measure is better than that achieved by the other measures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=document%20classification" title="document classification">document classification</a>, <a href="https://publications.waset.org/abstracts/search?q=document%20clustering" title=" document clustering"> document clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=entropy" title=" entropy"> entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=accuracy" title=" accuracy"> accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=classifiers" title=" classifiers"> classifiers</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering%20algorithms" title=" clustering algorithms"> clustering algorithms</a> </p> <a href="https://publications.waset.org/abstracts/22708/a-similarity-measure-for-classification-and-clustering-in-image-based-medical-and-text-based-banking-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22708.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">518</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1518</span> Semi-Supervised Hierarchical Clustering Given a Reference Tree of Labeled Documents</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ying%20Zhao">Ying Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Xingyan%20Bin"> Xingyan Bin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Semi-supervised clustering algorithms have been shown effective to improve clustering process with even limited supervision. However, semi-supervised hierarchical clustering remains challenging due to the complexities of expressing constraints for agglomerative clustering algorithms. This paper proposes novel semi-supervised agglomerative clustering algorithms to build a hierarchy based on a known reference tree. We prove that by enforcing distance constraints defined by a reference tree during the process of hierarchical clustering, the resultant tree is guaranteed to be consistent with the reference tree. We also propose a framework that allows the hierarchical tree generation be aware of levels of levels of the agglomerative tree under creation, so that metric weights can be learned and adopted at each level in a recursive fashion. The experimental evaluation shows that the additional cost of our contraint-based semi-supervised hierarchical clustering algorithm (HAC) is negligible, and our combined semi-supervised HAC algorithm outperforms the state-of-the-art algorithms on real-world datasets. The experiments also show that our proposed methods can improve clustering performance even with a small number of unevenly distributed labeled data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=semi-supervised%20clustering" title="semi-supervised clustering">semi-supervised clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%0D%0Aagglomerative%20clustering" title=" hierarchical agglomerative clustering"> hierarchical agglomerative clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=reference%20trees" title=" reference trees"> reference trees</a>, <a href="https://publications.waset.org/abstracts/search?q=distance%20constraints" title=" distance constraints "> distance constraints </a> </p> <a href="https://publications.waset.org/abstracts/19478/semi-supervised-hierarchical-clustering-given-a-reference-tree-of-labeled-documents" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19478.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">547</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1517</span> Sustainability and Clustering: A Bibliometric Assessment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fernanda%20M.%20Assef">Fernanda M. Assef</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20Teresinha%20A.%20Steiner"> Maria Teresinha A. Steiner</a>, <a href="https://publications.waset.org/abstracts/search?q=David%20Gabriel%20F.%20Barros"> David Gabriel F. Barros</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Review researches are useful in terms of analysis of research problems. Between the types of review documents, we commonly find bibliometric studies. This type of application often helps the global visualization of a research problem and helps academics worldwide to understand the context of a research area better. In this document, a bibliometric view surrounding clustering techniques and sustainability problems is presented. The authors aimed at which issues mostly use clustering techniques, and, even which sustainability issue would be more impactful on today’s moment of research. During the bibliometric analysis, we found ten different groups of research in clustering applications for sustainability issues: Energy; Environmental; Non-urban planning; Sustainable Development; Sustainable Supply Chain; Transport; Urban Planning; Water; Waste Disposal; and, Others. And, by analyzing the citations of each group, we discovered that the Environmental group could be classified as the most impactful research cluster in the area mentioned. Now, after the content analysis of each paper classified in the environmental group, we found that the k-means technique is preferred for solving sustainability problems with clustering methods since it appeared the most amongst the documents. The authors finally conclude that a bibliometric assessment could help indicate a gap of researches on waste disposal – which was the group with the least amount of publications – and the most impactful research on environmental problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bibliometric%20assessment" title="bibliometric assessment">bibliometric assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=sustainability" title=" sustainability"> sustainability</a>, <a href="https://publications.waset.org/abstracts/search?q=territorial%20partitioning" title=" territorial partitioning"> territorial partitioning</a> </p> <a href="https://publications.waset.org/abstracts/125972/sustainability-and-clustering-a-bibliometric-assessment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/125972.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">109</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1516</span> Mitigating the Negative Effect of Intrabrand Clustering: The Role of Interbrand Clustering and Firm Size</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Moeen%20Naseer%20Butt">Moeen Naseer Butt</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Clustering –geographic concentrations of entities– has recently received more attention in marketing research and has been shown to affect multiple outcomes. This study investigates the impact of intrabrand clustering (clustering of same-brand outlets) on an outlet’s quality performance. Further, it assesses the moderating effects of interbrand clustering (clustering of other-brand outlets) and firm size. An examination of approximately 21,000 food service establishments in New York State in 2019 finds that the impact of intrabrand clustering on an outlet’s quality performance is context-dependent. Specifically, intrabrand clustering decreases, whereas interbrand clustering and firm size help increase the outlet’s performance. Additionally, this study finds that the role of firm size is more substantial than interbrand clustering in mitigating the adverse effects of intrabrand clustering on outlet quality performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intraband%20clustering" title="intraband clustering">intraband clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=interbrand%20clustering" title=" interbrand clustering"> interbrand clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=firm%20size" title=" firm size"> firm size</a>, <a href="https://publications.waset.org/abstracts/search?q=brand%20competition" title=" brand competition"> brand competition</a>, <a href="https://publications.waset.org/abstracts/search?q=outlet%20performance" title=" outlet performance"> outlet performance</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20violations" title=" quality violations"> quality violations</a> </p> <a href="https://publications.waset.org/abstracts/155035/mitigating-the-negative-effect-of-intrabrand-clustering-the-role-of-interbrand-clustering-and-firm-size" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155035.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 paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1515</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/abstracts/search?q=Z.%20Abdullah">Z. Abdullah</a>, <a href="https://publications.waset.org/abstracts/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 areas in data mining and it can be classified into partition, hierarchical, density based, and grid-based. Therefore, in this paper, we do a survey and review for 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/abstracts/search?q=clustering" title="clustering">clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20learning" title=" unsupervised learning"> unsupervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithms" title=" algorithms"> algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical" title=" hierarchical"> hierarchical</a> </p> <a href="https://publications.waset.org/abstracts/31217/hierarchical-clustering-algorithms-in-data-mining" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31217.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">885</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1514</span> Progressive Multimedia Collection Structuring via Scene Linking</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aman%20Berhe">Aman Berhe</a>, <a href="https://publications.waset.org/abstracts/search?q=Camille%20Guinaudeau"> Camille Guinaudeau</a>, <a href="https://publications.waset.org/abstracts/search?q=Claude%20Barras"> Claude Barras</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to facilitate information seeking in large collections of multimedia documents with long and progressive content (such as broadcast news or TV series), one can extract the semantic links that exist between semantically coherent parts of documents, i.e., scenes. The links can then create a coherent collection of scenes from which it is easier to perform content analysis, topic extraction, or information retrieval. In this paper, we focus on TV series structuring and propose two approaches for scene linking at different levels of granularity (episode and season): a fuzzy online clustering technique and a graph-based community detection algorithm. When evaluated on the two first seasons of the TV series Game of Thrones, we found that the fuzzy online clustering approach performed better compared to graph-based community detection at the episode level, while graph-based approaches show better performance at the season level. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multimedia%20collection%20structuring" title="multimedia collection structuring">multimedia collection structuring</a>, <a href="https://publications.waset.org/abstracts/search?q=progressive%20content" title=" progressive content"> progressive content</a>, <a href="https://publications.waset.org/abstracts/search?q=scene%20linking" title=" scene linking"> scene linking</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20clustering" title=" fuzzy clustering"> fuzzy clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=community%20detection" title=" community detection"> community detection</a> </p> <a href="https://publications.waset.org/abstracts/153764/progressive-multimedia-collection-structuring-via-scene-linking" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153764.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">100</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1513</span> Hybrid Hierarchical Clustering Approach for Community Detection in Social Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Radhia%20Toujani">Radhia Toujani</a>, <a href="https://publications.waset.org/abstracts/search?q=Jalel%20Akaichi"> Jalel Akaichi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Social Networks generally present a hierarchy of communities. To determine these communities and the relationship between them, detection algorithms should be applied. Most of the existing algorithms, proposed for hierarchical communities identification, are based on either agglomerative clustering or divisive clustering. In this paper, we present a hybrid hierarchical clustering approach for community detection based on both bottom-up and bottom-down clustering. Obviously, our approach provides more relevant community structure than hierarchical method which considers only divisive or agglomerative clustering to identify communities. Moreover, we performed some comparative experiments to enhance the quality of the clustering results and to show the effectiveness of our algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=agglomerative%20hierarchical%20clustering" title="agglomerative hierarchical clustering">agglomerative hierarchical clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=community%20structure" title=" community structure"> community structure</a>, <a href="https://publications.waset.org/abstracts/search?q=divisive%20hierarchical%20clustering" title=" divisive hierarchical clustering"> divisive hierarchical clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20hierarchical%20clustering" title=" hybrid hierarchical clustering"> hybrid hierarchical clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=opinion%20mining" title=" opinion mining"> opinion mining</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20network" title=" social network"> social network</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20network%20analysis" title=" social network analysis"> social network analysis</a> </p> <a href="https://publications.waset.org/abstracts/63702/hybrid-hierarchical-clustering-approach-for-community-detection-in-social-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63702.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">365</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1512</span> Performance Analysis of Hierarchical Agglomerative Clustering in a Wireless Sensor Network Using Quantitative Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tapan%20Jain">Tapan Jain</a>, <a href="https://publications.waset.org/abstracts/search?q=Davender%20Singh%20Saini"> Davender Singh Saini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Clustering is a useful mechanism in wireless sensor networks which helps to cope with scalability and data transmission problems. The basic aim of our research work is to provide efficient clustering using Hierarchical agglomerative clustering (HAC). If the distance between the sensing nodes is calculated using their location then it’s quantitative HAC. This paper compares the various agglomerative clustering techniques applied in a wireless sensor network using the quantitative data. The simulations are done in MATLAB and the comparisons are made between the different protocols using dendrograms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=routing" title="routing">routing</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20clustering" title=" hierarchical clustering"> hierarchical clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=agglomerative" title=" agglomerative"> agglomerative</a>, <a href="https://publications.waset.org/abstracts/search?q=quantitative" title=" quantitative"> quantitative</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20sensor%20network" title=" wireless sensor network"> wireless sensor network</a> </p> <a href="https://publications.waset.org/abstracts/3593/performance-analysis-of-hierarchical-agglomerative-clustering-in-a-wireless-sensor-network-using-quantitative-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3593.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">615</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1511</span> Flowing Online Vehicle GPS Data Clustering Using a New Parallel K-Means Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Orhun%20Vural">Orhun Vural</a>, <a href="https://publications.waset.org/abstracts/search?q=Oguz%20%20Bayat"> Oguz Bayat</a>, <a href="https://publications.waset.org/abstracts/search?q=Rustu%20Akay"> Rustu Akay</a>, <a href="https://publications.waset.org/abstracts/search?q=Osman%20N.%20Ucan"> Osman N. Ucan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study presents a new parallel approach clustering of GPS data. Evaluation has been made by comparing execution time of various clustering algorithms on GPS data. This paper aims to propose a parallel based on neighborhood K-means algorithm to make it faster. The proposed parallelization approach assumes that each GPS data represents a vehicle and to communicate between vehicles close to each other after vehicles are clustered. This parallelization approach has been examined on different sized continuously changing GPS data and compared with serial K-means algorithm and other serial clustering algorithms. The results demonstrated that proposed parallel K-means algorithm has been shown to work much faster than other clustering algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=parallel%20k-means%20algorithm" title="parallel k-means algorithm">parallel k-means algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=parallel%20clustering" title=" parallel clustering"> parallel clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering%20algorithms" title=" clustering algorithms"> clustering algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering%20on%20flowing%20data" title=" clustering on flowing data"> clustering on flowing data</a> </p> <a href="https://publications.waset.org/abstracts/86622/flowing-online-vehicle-gps-data-clustering-using-a-new-parallel-k-means-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86622.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">221</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1510</span> Fuzzy Optimization Multi-Objective Clustering Ensemble Model for Multi-Source Data Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=C.%20B.%20Le">C. B. Le</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20N.%20Pham"> V. N. Pham</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In modern data analysis, multi-source data appears more and more in real applications. Multi-source data clustering has emerged as a important issue in the data mining and machine learning community. Different data sources provide information about different data. Therefore, multi-source data linking is essential to improve clustering performance. However, in practice multi-source data is often heterogeneous, uncertain, and large. This issue is considered a major challenge from multi-source data. Ensemble is a versatile machine learning model in which learning techniques can work in parallel, with big data. Clustering ensemble has been shown to outperform any standard clustering algorithm in terms of accuracy and robustness. However, most of the traditional clustering ensemble approaches are based on single-objective function and single-source data. This paper proposes a new clustering ensemble method for multi-source data analysis. The fuzzy optimized multi-objective clustering ensemble method is called FOMOCE. Firstly, a clustering ensemble mathematical model based on the structure of multi-objective clustering function, multi-source data, and dark knowledge is introduced. Then, rules for extracting dark knowledge from the input data, clustering algorithms, and base clusterings are designed and applied. Finally, a clustering ensemble algorithm is proposed for multi-source data analysis. The experiments were performed on the standard sample data set. The experimental results demonstrate the superior performance of the FOMOCE method compared to the existing clustering ensemble methods and multi-source clustering methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering%20ensemble" title="clustering ensemble">clustering ensemble</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-source" title=" multi-source"> multi-source</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective" title=" multi-objective"> multi-objective</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20clustering" title=" fuzzy clustering"> fuzzy clustering</a> </p> <a href="https://publications.waset.org/abstracts/136598/fuzzy-optimization-multi-objective-clustering-ensemble-model-for-multi-source-data-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136598.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">189</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1509</span> ACOPIN: An ACO Algorithm with TSP Approach for Clustering Proteins in Protein Interaction Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jamaludin%20Sallim">Jamaludin Sallim</a>, <a href="https://publications.waset.org/abstracts/search?q=Rozlina%20Mohamed"> Rozlina Mohamed</a>, <a href="https://publications.waset.org/abstracts/search?q=Roslina%20Abdul%20Hamid"> Roslina Abdul Hamid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we proposed an Ant Colony Optimization (ACO) algorithm together with Traveling Salesman Problem (TSP) approach to investigate the clustering problem in Protein Interaction Networks (PIN). We named this combination as ACOPIN. The purpose of this work is two-fold. First, to test the efficacy of ACO in clustering PIN and second, to propose the simple generalization of the ACO algorithm that might allow its application in clustering proteins in PIN. We split this paper to three main sections. First, we describe the PIN and clustering proteins in PIN. Second, we discuss the steps involved in each phase of ACO algorithm. Finally, we present some results of the investigation with the clustering patterns. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20optimization%20algorithm" title="ant colony optimization algorithm">ant colony optimization algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=searching%20algorithm" title=" searching algorithm"> searching algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20functional%20module" title=" protein functional module"> protein functional module</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20interaction%20network" title=" protein interaction network "> protein interaction network </a> </p> <a href="https://publications.waset.org/abstracts/22367/acopin-an-aco-algorithm-with-tsp-approach-for-clustering-proteins-in-protein-interaction-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22367.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">612</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1508</span> The Use of Technology in Mathematics Learning (1995-2024): A Bibliometric Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rahma%20Adinda%20Sartika">Rahma Adinda Sartika</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of technology in learning mathematics has received a positive response from both students and teachers, so many researchers have conducted research on this theme. Based on the findings carried out in this study, 807 documents relevant to this theme have been published in Scopus from 1995-2024. After going through the stages of identification, screening, eligibility, and including, the documents that meet the criteria are 227 documents. These documents are then analyzed using the bibliometric method so that it can be seen that the most published documents in the Scopus database occurred in 2020, with 38 documents, and the lowest was from 1996 to 2000 and 2004 to 2007, namely, no documents published. The highest number of citations is in documents published in 2018, with a total of 349 citations, so the h-index is higher than the others. The country that published the most documents relevant to this theme is Indonesia with a total of 91 documents. The second largest is the United States, with a total of 28 published documents, and the third largest is China, with a total of 15 documents. Indonesia and the United States have the most working relationships between countries compared to other countries. The focus of research related to this theme is 1) mathematics learning, 2) learning systems, 3) engineering education, 4) technology and 5) mathematical concepts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=technology" title="technology">technology</a>, <a href="https://publications.waset.org/abstracts/search?q=bibliometric" title=" bibliometric"> bibliometric</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematics%20learning" title=" mathematics learning"> mathematics learning</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20concepts" title=" mathematical concepts"> mathematical concepts</a> </p> <a href="https://publications.waset.org/abstracts/188207/the-use-of-technology-in-mathematics-learning-1995-2024-a-bibliometric-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/188207.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">56</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1507</span> Spectral Clustering for Manufacturing Cell Formation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yessica%20Nataliani">Yessica Nataliani</a>, <a href="https://publications.waset.org/abstracts/search?q=Miin-Shen%20Yang"> Miin-Shen Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cell formation (CF) is an important step in group technology. It is used in designing cellular manufacturing systems using similarities between parts in relation to machines so that it can identify part families and machine groups. There are many CF methods in the literature, but there is less spectral clustering used in CF. In this paper, we propose a spectral clustering algorithm for machine-part CF. Some experimental examples are used to illustrate its efficiency. Overall, the spectral clustering algorithm can be used in CF with a wide variety of machine/part matrices. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=group%20technology" title="group technology">group technology</a>, <a href="https://publications.waset.org/abstracts/search?q=cell%20formation" title=" cell formation"> cell formation</a>, <a href="https://publications.waset.org/abstracts/search?q=spectral%20clustering" title=" spectral clustering"> spectral clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=grouping%20efficiency" title=" grouping efficiency"> grouping efficiency</a> </p> <a href="https://publications.waset.org/abstracts/72294/spectral-clustering-for-manufacturing-cell-formation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72294.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">407</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1506</span> Investigation of Clustering Algorithms Used in Wireless Sensor Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Naim%20Karasekreter">Naim Karasekreter</a>, <a href="https://publications.waset.org/abstracts/search?q=Ugur%20Fidan"> Ugur Fidan</a>, <a href="https://publications.waset.org/abstracts/search?q=Fatih%20Basciftci"> Fatih Basciftci</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wireless sensor networks are networks in which more than one sensor node is organized among themselves. The working principle is based on the transfer of the sensed data over the other nodes in the network to the central station. Wireless sensor networks concentrate on routing algorithms, energy efficiency and clustering algorithms. In the clustering method, the nodes in the network are divided into clusters using different parameters and the most suitable cluster head is selected from among them. The data to be sent to the center is sent per cluster, and the cluster head is transmitted to the center. With this method, the network traffic is reduced and the energy efficiency of the nodes is increased. In this study, clustering algorithms were examined in terms of clustering performances and cluster head selection characteristics to try to identify weak and strong sides. This work is supported by the Project 17.Kariyer.123 of Afyon Kocatepe University BAP Commission. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wireless%20sensor%20networks%20%28WSN%29" title="wireless sensor networks (WSN)">wireless sensor networks (WSN)</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering%20algorithm" title=" clustering algorithm"> clustering algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=cluster%20head" title=" cluster head"> cluster head</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a> </p> <a href="https://publications.waset.org/abstracts/78846/investigation-of-clustering-algorithms-used-in-wireless-sensor-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78846.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">513</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1505</span> A Comparative Study of Multi-SOM Algorithms for Determining the Optimal Number of Clusters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Im%C3%A8n%20Khanchouch">Imèn Khanchouch</a>, <a href="https://publications.waset.org/abstracts/search?q=Malika%20Charrad"> Malika Charrad</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Limam"> Mohamed Limam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The interpretation of the quality of clusters and the determination of the optimal number of clusters is still a crucial problem in clustering. We focus in this paper on multi-SOM clustering method which overcomes the problem of extracting the number of clusters from the SOM map through the use of a clustering validity index. We then tested multi-SOM using real and artificial data sets with different evaluation criteria not used previously such as Davies Bouldin index, Dunn index and silhouette index. The developed multi-SOM algorithm is compared to k-means and Birch methods. Results show that it is more efficient than classical clustering methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering" title="clustering">clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=SOM" title=" SOM"> SOM</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-SOM" title=" multi-SOM"> multi-SOM</a>, <a href="https://publications.waset.org/abstracts/search?q=DB%20index" title=" DB index"> DB index</a>, <a href="https://publications.waset.org/abstracts/search?q=Dunn%20index" title=" Dunn index"> Dunn index</a>, <a href="https://publications.waset.org/abstracts/search?q=silhouette%20index" title=" silhouette index"> silhouette index</a> </p> <a href="https://publications.waset.org/abstracts/17422/a-comparative-study-of-multi-som-algorithms-for-determining-the-optimal-number-of-clusters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17422.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">599</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1504</span> A Fuzzy Kernel K-Medoids Algorithm for Clustering Uncertain Data Objects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Behnam%20Tavakkol">Behnam Tavakkol</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Uncertain data mining algorithms use different ways to consider uncertainty in data such as by representing a data object as a sample of points or a probability distribution. Fuzzy methods have long been used for clustering traditional (certain) data objects. They are used to produce non-crisp cluster labels. For uncertain data, however, besides some uncertain fuzzy k-medoids algorithms, not many other fuzzy clustering methods have been developed. In this work, we develop a fuzzy kernel k-medoids algorithm for clustering uncertain data objects. The developed fuzzy kernel k-medoids algorithm is superior to existing fuzzy k-medoids algorithms in clustering data sets with non-linearly separable clusters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering%20algorithm" title="clustering algorithm">clustering algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20methods" title=" fuzzy methods"> fuzzy methods</a>, <a href="https://publications.waset.org/abstracts/search?q=kernel%20k-medoids" title=" kernel k-medoids"> kernel k-medoids</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertain%20data" title=" uncertain data"> uncertain data</a> </p> <a href="https://publications.waset.org/abstracts/123501/a-fuzzy-kernel-k-medoids-algorithm-for-clustering-uncertain-data-objects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/123501.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">215</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1503</span> Sentiment Classification of Documents</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Swarnadip%20Ghosh">Swarnadip Ghosh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sentiment Analysis is the process of detecting the contextual polarity of text. In other words, it determines whether a piece of writing is positive, negative or neutral.Sentiment analysis of documents holds great importance in today's world, when numerous information is stored in databases and in the world wide web. An efficient algorithm to illicit such information, would be beneficial for social, economic as well as medical purposes. In this project, we have developed an algorithm to classify a document into positive or negative. Using our algorithm, we obtained a feature set from the data, and classified the documents based on this feature set. It is important to note that, in the classification, we have not used the independence assumption, which is considered by many procedures like the Naive Bayes. This makes the algorithm more general in scope. Moreover, because of the sparsity and high dimensionality of such data, we did not use empirical distribution for estimation, but developed a method by finding degree of close clustering of the data points. We have applied our algorithm on a movie review data set obtained from IMDb and obtained satisfactory results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sentiment" title="sentiment">sentiment</a>, <a href="https://publications.waset.org/abstracts/search?q=Run%27s%20Test" title=" Run's Test"> Run's Test</a>, <a href="https://publications.waset.org/abstracts/search?q=cross%20validation" title=" cross validation"> cross validation</a>, <a href="https://publications.waset.org/abstracts/search?q=higher%20dimensional%20pmf%20estimation" title=" higher dimensional pmf estimation"> higher dimensional pmf estimation</a> </p> <a href="https://publications.waset.org/abstracts/19401/sentiment-classification-of-documents" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19401.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">402</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1502</span> An Experimental Study on Some Conventional and Hybrid Models of Fuzzy Clustering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jeugert%20Kujtila">Jeugert Kujtila</a>, <a href="https://publications.waset.org/abstracts/search?q=Kristi%20Hoxhalli"> Kristi Hoxhalli</a>, <a href="https://publications.waset.org/abstracts/search?q=Ramazan%20Dalipi"> Ramazan Dalipi</a>, <a href="https://publications.waset.org/abstracts/search?q=Erjon%20Cota"> Erjon Cota</a>, <a href="https://publications.waset.org/abstracts/search?q=Ardit%20Murati"> Ardit Murati</a>, <a href="https://publications.waset.org/abstracts/search?q=Erind%20Bedalli"> Erind Bedalli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Clustering is a versatile instrument in the analysis of collections of data providing insights of the underlying structures of the dataset and enhancing the modeling capabilities. The fuzzy approach to the clustering problem increases the flexibility involving the concept of partial memberships (some value in the continuous interval [0, 1]) of the instances in the clusters. Several fuzzy clustering algorithms have been devised like FCM, Gustafson-Kessel, Gath-Geva, kernel-based FCM, PCM etc. Each of these algorithms has its own advantages and drawbacks, so none of these algorithms would be able to perform superiorly in all datasets. In this paper we will experimentally compare FCM, GK, GG algorithm and a hybrid two-stage fuzzy clustering model combining the FCM and Gath-Geva algorithms. Firstly we will theoretically dis-cuss the advantages and drawbacks for each of these algorithms and we will describe the hybrid clustering model exploiting the advantages and diminishing the drawbacks of each algorithm. Secondly we will experimentally compare the accuracy of the hybrid model by applying it on several benchmark and synthetic datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20clustering" title="fuzzy clustering">fuzzy clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20c-means%20algorithm%20%28FCM%29" title=" fuzzy c-means algorithm (FCM)"> fuzzy c-means algorithm (FCM)</a>, <a href="https://publications.waset.org/abstracts/search?q=Gustafson-Kessel%20algorithm" title=" Gustafson-Kessel algorithm"> Gustafson-Kessel algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20clustering%20model" title=" hybrid clustering model"> hybrid clustering model</a> </p> <a href="https://publications.waset.org/abstracts/67863/an-experimental-study-on-some-conventional-and-hybrid-models-of-fuzzy-clustering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67863.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">514</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1501</span> Words Spotting in the Images Handwritten Historical Documents </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Issam%20Ben%20Jami">Issam Ben Jami </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Information retrieval in digital libraries is very important because most famous historical documents occupy a significant value. The word spotting in historical documents is a very difficult notion, because automatic recognition of such documents is naturally cursive, it represents a wide variability in the level scale and translation words in the same documents. We first present a system for the automatic recognition, based on the extraction of interest points words from the image model. The extraction phase of the key points is chosen from the representation of the image as a synthetic description of the shape recognition in a multidimensional space. As a result, we use advanced methods that can find and describe interesting points invariant to scale, rotation and lighting which are linked to local configurations of pixels. We test this approach on documents of the 15th century. Our experiments give important results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20matching" title="feature matching">feature matching</a>, <a href="https://publications.waset.org/abstracts/search?q=historical%20documents" title=" historical documents"> historical documents</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=word%20spotting" title=" word spotting"> word spotting</a> </p> <a href="https://publications.waset.org/abstracts/52183/words-spotting-in-the-images-handwritten-historical-documents" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52183.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">274</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1500</span> Improved K-Means Clustering Algorithm Using RHadoop with Combiner</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ji%20Eun%20Shin">Ji Eun Shin</a>, <a href="https://publications.waset.org/abstracts/search?q=Dong%20Hoon%20Lim"> Dong Hoon Lim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data clustering is a common technique used in data analysis and is used in many applications, such as artificial intelligence, pattern recognition, economics, ecology, psychiatry and marketing. K-means clustering is a well-known clustering algorithm aiming to cluster a set of data points to a predefined number of clusters. In this paper, we implement K-means algorithm based on MapReduce framework with RHadoop to make the clustering method applicable to large scale data. RHadoop is a collection of R packages that allow users to manage and analyze data with Hadoop. The main idea is to introduce a combiner as a function of our map output to decrease the amount of data needed to be processed by reducers. The experimental results demonstrated that K-means algorithm using RHadoop can scale well and efficiently process large data sets on commodity hardware. We also showed that our K-means algorithm using RHadoop with combiner was faster than regular algorithm without combiner as the size of data set increases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=combiner" title=" combiner"> combiner</a>, <a href="https://publications.waset.org/abstracts/search?q=K-means%20clustering" title=" K-means clustering"> K-means clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=RHadoop" title=" RHadoop"> RHadoop</a> </p> <a href="https://publications.waset.org/abstracts/41570/improved-k-means-clustering-algorithm-using-rhadoop-with-combiner" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41570.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">438</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1499</span> Modified Active (MA) Algorithm to Generate Semantic Web Related Clustered Hierarchy for Keyword Search</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=G.%20Leena%20Giri">G. Leena Giri</a>, <a href="https://publications.waset.org/abstracts/search?q=Archana%20Mathur"> Archana Mathur</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20H.%20Manjula"> S. H. Manjula</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20R.%20Venugopal"> K. R. Venugopal</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20M.%20Patnaik"> L. M. Patnaik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Keyword search in XML documents is based on the notion of lowest common ancestors in the labelled trees model of XML documents and has recently gained a lot of research interest in the database community. In this paper, we propose the Modified Active (MA) algorithm which is an improvement over the active clustering algorithm by taking into consideration the entity aspect of the nodes to find the level of the node pertaining to a particular keyword input by the user. A portion of the bibliography database is used to experimentally evaluate the modified active algorithm and results show that it performs better than the active algorithm. Our modification improves the response time of the system and thereby increases the efficiency of the system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=keyword%20matching%20patterns" title="keyword matching patterns">keyword matching patterns</a>, <a href="https://publications.waset.org/abstracts/search?q=MA%20algorithm" title=" MA algorithm"> MA algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20search" title=" semantic search"> semantic search</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20management" title=" knowledge management"> knowledge management</a> </p> <a href="https://publications.waset.org/abstracts/6608/modified-active-ma-algorithm-to-generate-semantic-web-related-clustered-hierarchy-for-keyword-search" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6608.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">413</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1498</span> Application of Data Mining for Aquifer Environmental Assessment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saman%20Javadi">Saman Javadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mehdi%20Hashemy"> Mehdi Hashemy</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohahammad%20Mahmoodi"> Mohahammad Mahmoodi </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Vulnerability maps are employed as an important solution in order to handle entrance of pollution into the aquifers. The common way to provide vulnerability map is DRASTIC. Meanwhile, application of the method is not easy to apply for any aquifer due to choosing appropriate constant values of weights and ranks. In this study, a new approach using k-means clustering is applied to make vulnerability maps. Four features of depth to groundwater, hydraulic conductivity, recharge value and vadose zone were considered at the same time as features of clustering. Five regions are recognized out of the case study represent zones with different level of vulnerability. The finding results show that clustering provides a realistic vulnerability map so that, Pearson’s correlation coefficients between nitrate concentrations and clustering vulnerability is obtained 61%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering" title="clustering">clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=groundwater" title=" groundwater"> groundwater</a>, <a href="https://publications.waset.org/abstracts/search?q=vulnerability%20assessment" title=" vulnerability assessment "> vulnerability assessment </a> </p> <a href="https://publications.waset.org/abstracts/20164/application-of-data-mining-for-aquifer-environmental-assessment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20164.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">603</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1497</span> 3D Mesh Coarsening via Uniform Clustering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shuhua%20Lai">Shuhua Lai</a>, <a href="https://publications.waset.org/abstracts/search?q=Kairui%20Chen"> Kairui Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a fast and efficient mesh coarsening algorithm for 3D triangular meshes. Theis approach can be applied to very complex 3D meshes of arbitrary topology and with millions of vertices. The algorithm is based on the clustering of the input mesh elements, which divides the faces of an input mesh into a given number of clusters for clustering purpose by approximating the Centroidal Voronoi Tessellation of the input mesh. Once a clustering is achieved, it provides us an efficient way to construct uniform tessellations, and therefore leads to good coarsening of polygonal meshes. With proliferation of 3D scanners, this coarsening algorithm is particularly useful for reverse engineering applications of 3D models, which in many cases are dense, non-uniform, irregular and arbitrary topology. Examples demonstrating effectiveness of the new algorithm are also included in the paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=coarsening" title="coarsening">coarsening</a>, <a href="https://publications.waset.org/abstracts/search?q=mesh%20clustering" title=" mesh clustering"> mesh clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=shape%20approximation" title=" shape approximation"> shape approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=mesh%20simplification" title=" mesh simplification"> mesh simplification</a> </p> <a href="https://publications.waset.org/abstracts/48919/3d-mesh-coarsening-via-uniform-clustering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48919.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">380</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1496</span> Multimodal Optimization of Density-Based Clustering Using Collective Animal Behavior Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kristian%20Bautista">Kristian Bautista</a>, <a href="https://publications.waset.org/abstracts/search?q=Ruben%20A.%20Idoy"> Ruben A. Idoy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A bio-inspired metaheuristic algorithm inspired by the theory of collective animal behavior (CAB) was integrated to density-based clustering modeled as multimodal optimization problem. The algorithm was tested on synthetic, Iris, Glass, Pima and Thyroid data sets in order to measure its effectiveness relative to CDE-based Clustering algorithm. Upon preliminary testing, it was found out that one of the parameter settings used was ineffective in performing clustering when applied to the algorithm prompting the researcher to do an investigation. It was revealed that fine tuning distance δ3 that determines the extent to which a given data point will be clustered helped improve the quality of cluster output. Even though the modification of distance δ3 significantly improved the solution quality and cluster output of the algorithm, results suggest that there is no difference between the population mean of the solutions obtained using the original and modified parameter setting for all data sets. This implies that using either the original or modified parameter setting will not have any effect towards obtaining the best global and local animal positions. Results also suggest that CDE-based clustering algorithm is better than CAB-density clustering algorithm for all data sets. Nevertheless, CAB-density clustering algorithm is still a good clustering algorithm because it has correctly identified the number of classes of some data sets more frequently in a thirty trial run with a much smaller standard deviation, a potential in clustering high dimensional data sets. Thus, the researcher recommends further investigation in the post-processing stage of the algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering" title="clustering">clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=metaheuristics" title=" metaheuristics"> metaheuristics</a>, <a href="https://publications.waset.org/abstracts/search?q=collective%20animal%20behavior%20algorithm" title=" collective animal behavior algorithm"> collective animal behavior algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=density-based%20%20clustering" title=" density-based clustering"> density-based clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=multimodal%20optimization" title=" multimodal optimization"> multimodal optimization</a> </p> <a href="https://publications.waset.org/abstracts/94254/multimodal-optimization-of-density-based-clustering-using-collective-animal-behavior-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94254.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">230</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1495</span> Anomaly Detection Based Fuzzy K-Mode Clustering for Categorical Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Murat%20Yazici">Murat Yazici</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Anomalies are irregularities found in data that do not adhere to a well-defined standard of normal behavior. The identification of outliers or anomalies in data has been a subject of study within the statistics field since the 1800s. Over time, a variety of anomaly detection techniques have been developed in several research communities. The cluster analysis can be used to detect anomalies. It is the process of associating data with clusters that are as similar as possible while dissimilar clusters are associated with each other. Many of the traditional cluster algorithms have limitations in dealing with data sets containing categorical properties. To detect anomalies in categorical data, fuzzy clustering approach can be used with its advantages. The fuzzy k-Mode (FKM) clustering algorithm, which is one of the fuzzy clustering approaches, by extension to the k-means algorithm, is reported for clustering datasets with categorical values. It is a form of clustering: each point can be associated with more than one cluster. In this paper, anomaly detection is performed on two simulated data by using the FKM cluster algorithm. As a significance of the study, the FKM cluster algorithm allows to determine anomalies with their abnormality degree in contrast to numerous anomaly detection algorithms. According to the results, the FKM cluster algorithm illustrated good performance in the anomaly detection of data, including both one anomaly and more than one anomaly. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20k-mode%20clustering" title="fuzzy k-mode clustering">fuzzy k-mode clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=anomaly%20detection" title=" anomaly detection"> anomaly detection</a>, <a href="https://publications.waset.org/abstracts/search?q=noise" title=" noise"> noise</a>, <a href="https://publications.waset.org/abstracts/search?q=categorical%20data" title=" categorical data"> categorical data</a> </p> <a href="https://publications.waset.org/abstracts/185305/anomaly-detection-based-fuzzy-k-mode-clustering-for-categorical-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185305.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">53</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1494</span> Chemical Reaction Algorithm for Expectation Maximization Clustering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Li%20Ni">Li Ni</a>, <a href="https://publications.waset.org/abstracts/search?q=Pen%20ManMan"> Pen ManMan</a>, <a href="https://publications.waset.org/abstracts/search?q=Li%20KenLi"> Li KenLi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Clustering is an intensive research for some years because of its multifaceted applications, such as biology, information retrieval, medicine, business and so on. The expectation maximization (EM) is a kind of algorithm framework in clustering methods, one of the ten algorithms of machine learning. Traditionally, optimization of objective function has been the standard approach in EM. Hence, research has investigated the utility of evolutionary computing and related techniques in the regard. Chemical Reaction Optimization (CRO) is a recently established method. So the property embedded in CRO is used to solve optimization problems. This paper presents an algorithm framework (EM-CRO) with modified CRO operators based on EM cluster problems. The hybrid algorithm is mainly to solve the problem of initial value sensitivity of the objective function optimization clustering algorithm. Our experiments mainly take the EM classic algorithm:k-means and fuzzy k-means as an example, through the CRO algorithm to optimize its initial value, get K-means-CRO and FKM-CRO algorithm. The experimental results of them show that there is improved efficiency for solving objective function optimization clustering problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chemical%20reaction%20optimization" title="chemical reaction optimization">chemical reaction optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=expection%20maimization" title=" expection maimization"> expection maimization</a>, <a href="https://publications.waset.org/abstracts/search?q=initia" title=" initia"> initia</a>, <a href="https://publications.waset.org/abstracts/search?q=objective%20function%20clustering" title=" objective function clustering"> objective function clustering</a> </p> <a href="https://publications.waset.org/abstracts/54706/chemical-reaction-algorithm-for-expectation-maximization-clustering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54706.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">713</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1493</span> Self-Supervised Attributed Graph Clustering with Dual Contrastive Loss Constraints</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lijuan%20Zhou">Lijuan Zhou</a>, <a href="https://publications.waset.org/abstracts/search?q=Mengqi%20Wu"> Mengqi Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Changyong%20Niu"> Changyong Niu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Attributed graph clustering can utilize the graph topology and node attributes to uncover hidden community structures and patterns in complex networks, aiding in the understanding and analysis of complex systems. Utilizing contrastive learning for attributed graph clustering can effectively exploit meaningful implicit relationships between data. However, existing attributed graph clustering methods based on contrastive learning suffer from the following drawbacks: 1) Complex data augmentation increases computational cost, and inappropriate data augmentation may lead to semantic drift. 2) The selection of positive and negative samples neglects the intrinsic cluster structure learned from graph topology and node attributes. Therefore, this paper proposes a method called self-supervised Attributed Graph Clustering with Dual Contrastive Loss constraints (AGC-DCL). Firstly, Siamese Multilayer Perceptron (MLP) encoders are employed to generate two views separately to avoid complex data augmentation. Secondly, the neighborhood contrastive loss is introduced to constrain node representation using local topological structure while effectively embedding attribute information through attribute reconstruction. Additionally, clustering-oriented contrastive loss is applied to fully utilize clustering information in global semantics for discriminative node representations, regarding the cluster centers from two views as negative samples to fully leverage effective clustering information from different views. Comparative clustering results with existing attributed graph clustering algorithms on six datasets demonstrate the superiority of the proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=attributed%20graph%20clustering" title="attributed graph clustering">attributed graph clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=contrastive%20learning" title=" contrastive learning"> contrastive learning</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering-oriented" title=" clustering-oriented"> clustering-oriented</a>, <a href="https://publications.waset.org/abstracts/search?q=self-supervised%20learning" title=" self-supervised learning"> self-supervised learning</a> </p> <a href="https://publications.waset.org/abstracts/185262/self-supervised-attributed-graph-clustering-with-dual-contrastive-loss-constraints" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185262.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">53</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1492</span> Data Gathering and Analysis for Arabic Historical Documents</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Dulla">Ali Dulla</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces a new dataset (and the methodology used to generate it) based on a wide range of historical Arabic documents containing clean data simple and homogeneous-page layouts. The experiments are implemented on printed and handwritten documents obtained respectively from some important libraries such as Qatar Digital Library, the British Library and the Library of Congress. We have gathered and commented on 150 archival document images from different locations and time periods. It is based on different documents from the 17th-19th century. The dataset comprises differing page layouts and degradations that challenge text line segmentation methods. Ground truth is produced using the Aletheia tool by PRImA and stored in an XML representation, in the PAGE (Page Analysis and Ground truth Elements) format. The dataset presented will be easily available to researchers world-wide for research into the obstacles facing various historical Arabic documents such as geometric correction of historical Arabic documents. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dataset%20production" title="dataset production">dataset production</a>, <a href="https://publications.waset.org/abstracts/search?q=ground%20truth%20production" title=" ground truth production"> ground truth production</a>, <a href="https://publications.waset.org/abstracts/search?q=historical%20documents" title=" historical documents"> historical documents</a>, <a href="https://publications.waset.org/abstracts/search?q=arbitrary%20warping" title=" arbitrary warping"> arbitrary warping</a>, <a href="https://publications.waset.org/abstracts/search?q=geometric%20correction" title=" geometric correction"> geometric correction</a> </p> <a href="https://publications.waset.org/abstracts/90467/data-gathering-and-analysis-for-arabic-historical-documents" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90467.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">168</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1491</span> Decision Trees Constructing Based on K-Means Clustering Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Loai%20Abdallah">Loai Abdallah</a>, <a href="https://publications.waset.org/abstracts/search?q=Malik%20Yousef"> Malik Yousef</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A domain space for the data should reflect the actual similarity between objects. Since objects belonging to the same cluster usually share some common traits even though their geometric distance might be relatively large. In general, the Euclidean distance of data points that represented by large number of features is not capturing the actual relation between those points. In this study, we propose a new method to construct a different space that is based on clustering to form a new distance metric. The new distance space is based on ensemble clustering (EC). The EC distance space is defined by tracking the membership of the points over multiple runs of clustering algorithm metric. Over this distance, we train the decision trees classifier (DT-EC). The results obtained by applying DT-EC on 10 datasets confirm our hypotheses that embedding the EC space as a distance metric would improve the performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ensemble%20clustering" title="ensemble clustering">ensemble clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20trees" title=" decision trees"> decision trees</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=K%20nearest%20neighbors" title=" K nearest neighbors"> K nearest neighbors</a> </p> <a href="https://publications.waset.org/abstracts/89656/decision-trees-constructing-based-on-k-means-clustering-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89656.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">190</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/abstracts/search?q=documents%20clustering&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=documents%20clustering&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=documents%20clustering&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=documents%20clustering&page=5">5</a></li> <li class="page-item"><a class="page-link" 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