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clustering</title> <meta name="description" content="Search results for: hierarchical clustering"> <meta name="keywords" content="hierarchical 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 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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="hierarchical 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> 1116</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: hierarchical clustering</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1116</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">1115</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">1114</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">1113</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">1112</span> Agglomerative Hierarchical Clustering Using the Tθ Family of Similarity Measures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Salima%20Kouici">Salima Kouici</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelkader%20Khelladi"> Abdelkader Khelladi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, we begin with the presentation of the Tθ family of usual similarity measures concerning multidimensional binary data. Subsequently, some properties of these measures are proposed. Finally, the impact of the use of different inter-elements measures on the results of the Agglomerative Hierarchical Clustering Methods is studied. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=binary%20data" title="binary data">binary data</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20measure" title=" similarity measure"> similarity measure</a>, <a href="https://publications.waset.org/abstracts/search?q=T%CE%B8%20measures" title=" Tθ measures"> Tθ measures</a>, <a href="https://publications.waset.org/abstracts/search?q=agglomerative%20hierarchical%20clustering" title=" agglomerative hierarchical clustering"> agglomerative hierarchical clustering</a> </p> <a href="https://publications.waset.org/abstracts/13108/agglomerative-hierarchical-clustering-using-the-tth-family-of-similarity-measures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13108.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">481</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">1111</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">1110</span> Finding Bicluster on Gene Expression Data of Lymphoma Based on Singular Value Decomposition and Hierarchical Clustering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alhadi%20Bustaman">Alhadi Bustaman</a>, <a href="https://publications.waset.org/abstracts/search?q=Soeganda%20Formalidin"> Soeganda Formalidin</a>, <a href="https://publications.waset.org/abstracts/search?q=Titin%20Siswantining"> Titin Siswantining</a> </p> <p class="card-text"><strong>Abstract:</strong></p> DNA microarray technology is used to analyze thousand gene expression data simultaneously and a very important task for drug development and test, function annotation, and cancer diagnosis. Various clustering methods have been used for analyzing gene expression data. However, when analyzing very large and heterogeneous collections of gene expression data, conventional clustering methods often cannot produce a satisfactory solution. Biclustering algorithm has been used as an alternative approach to identifying structures from gene expression data. In this paper, we introduce a transform technique based on singular value decomposition to identify normalized matrix of gene expression data followed by Mixed-Clustering algorithm and the Lift algorithm, inspired in the node-deletion and node-addition phases proposed by Cheng and Church based on Agglomerative Hierarchical Clustering (AHC). Experimental study on standard datasets demonstrated the effectiveness of the algorithm in gene expression data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=agglomerative%20hierarchical%20clustering%20%28AHC%29" title="agglomerative hierarchical clustering (AHC)">agglomerative hierarchical clustering (AHC)</a>, <a href="https://publications.waset.org/abstracts/search?q=biclustering" title=" biclustering"> biclustering</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20expression%20data" title=" gene expression data"> gene expression data</a>, <a href="https://publications.waset.org/abstracts/search?q=lymphoma" title=" lymphoma"> lymphoma</a>, <a href="https://publications.waset.org/abstracts/search?q=singular%20value%20decomposition%20%28SVD%29" title=" singular value decomposition (SVD)"> singular value decomposition (SVD)</a> </p> <a href="https://publications.waset.org/abstracts/72889/finding-bicluster-on-gene-expression-data-of-lymphoma-based-on-singular-value-decomposition-and-hierarchical-clustering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72889.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">278</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">1109</span> A Model Based Metaheuristic for Hybrid Hierarchical Community Structure in Social Networks</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> In recent years, the study of community detection in social networks has received great attention. The hierarchical structure of the network leads to the emergence of the convergence to a locally optimal community structure. In this paper, we aim to avoid this local optimum in the introduced hybrid hierarchical method. To achieve this purpose, we present an objective function where we incorporate the value of structural and semantic similarity based modularity and a metaheuristic namely bees colonies algorithm to optimize our objective function on both hierarchical level divisive and agglomerative. In order to assess the efficiency and the accuracy of the introduced hybrid bee colony model, we perform an extensive experimental evaluation on both synthetic and real networks. <p class="card-text"><strong>Keywords:</strong> <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=community%20detection" title=" community detection"> community detection</a>, <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=divisive%20hierarchical%20clustering" title=" divisive hierarchical clustering"> divisive hierarchical clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity" title=" similarity"> similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=modularity" title=" modularity"> modularity</a>, <a href="https://publications.waset.org/abstracts/search?q=metaheuristic" title=" metaheuristic"> metaheuristic</a>, <a href="https://publications.waset.org/abstracts/search?q=bee%20colony" title=" bee colony"> bee colony</a> </p> <a href="https://publications.waset.org/abstracts/64745/a-model-based-metaheuristic-for-hybrid-hierarchical-community-structure-in-social-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64745.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">379</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">1108</span> Electricity Generation from Renewables and Targets: An Application of Multivariate Statistical Techniques </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Filiz%20Ersoz">Filiz Ersoz</a>, <a href="https://publications.waset.org/abstracts/search?q=Taner%20Ersoz"> Taner Ersoz</a>, <a href="https://publications.waset.org/abstracts/search?q=Tugrul%20Bayraktar"> Tugrul Bayraktar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Renewable energy is referred to as "clean energy" and common popular support for the use of renewable energy (RE) is to provide electricity with zero carbon dioxide emissions. This study provides useful insight into the European Union (EU) RE, especially, into electricity generation obtained from renewables, and their targets. The objective of this study is to identify groups of European countries, using multivariate statistical analysis and selected indicators. The hierarchical clustering method is used to decide the number of clusters for EU countries. The conducted statistical hierarchical cluster analysis is based on the Ward’s clustering method and squared Euclidean distances. Hierarchical cluster analysis identified eight distinct clusters of European countries. Then, non-hierarchical clustering (k-means) method was applied. Discriminant analysis was used to determine the validity of the results with data normalized by Z score transformation. To explore the relationship between the selected indicators, correlation coefficients were computed. The results of the study reveal the current situation of RE in European Union Member States. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=share%20of%20electricity%20generation" title="share of electricity generation">share of electricity generation</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=discriminant" title=" discriminant"> discriminant</a>, <a href="https://publications.waset.org/abstracts/search?q=CO2%20emission" title=" CO2 emission"> CO2 emission</a> </p> <a href="https://publications.waset.org/abstracts/53237/electricity-generation-from-renewables-and-targets-an-application-of-multivariate-statistical-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53237.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">415</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">1107</span> Hierarchical Cluster Analysis of Raw Milk Samples Obtained from Organic and Conventional Dairy Farming in Autonomous Province of Vojvodina, Serbia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lidija%20Jevri%C4%87">Lidija Jevrić</a>, <a href="https://publications.waset.org/abstracts/search?q=Denis%20Ku%C4%8Devi%C4%87"> Denis Kučević</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanja%20Podunavac-Kuzmanovi%C4%87"> Sanja Podunavac-Kuzmanović</a>, <a href="https://publications.waset.org/abstracts/search?q=Strahinja%20Kova%C4%8Devi%C4%87"> Strahinja Kovačević</a>, <a href="https://publications.waset.org/abstracts/search?q=Milica%20Karad%C5%BEi%C4%87"> Milica Karadžić</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the present study, the Hierarchical Cluster Analysis (HCA) was applied in order to determine the differences between the milk samples originating from a conventional dairy farm (CF) and an organic dairy farm (OF) in AP Vojvodina, Republic of Serbia. The clustering was based on the basis of the average values of saturated fatty acids (SFA) content and unsaturated fatty acids (UFA) content obtained for every season. Therefore, the HCA included the annual SFA and UFA content values. The clustering procedure was carried out on the basis of Euclidean distances and Single linkage algorithm. The obtained dendrograms indicated that the clustering of UFA in OF was much more uniform compared to clustering of UFA in CF. In OF, spring stands out from the other months of the year. The same case can be noticed for CF, where winter is separated from the other months. The results could be expected because the composition of fatty acids content is greatly influenced by the season and nutrition of dairy cows during the year. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chemometrics" title="chemometrics">chemometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=food%20engineering" title=" food engineering"> food engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=milk%20quality" title=" milk quality"> milk quality</a> </p> <a href="https://publications.waset.org/abstracts/74573/hierarchical-cluster-analysis-of-raw-milk-samples-obtained-from-organic-and-conventional-dairy-farming-in-autonomous-province-of-vojvodina-serbia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74573.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">281</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">1106</span> Communication of Sensors in Clustering for Wireless Sensor Networks </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kashish%20Sareen">Kashish Sareen</a>, <a href="https://publications.waset.org/abstracts/search?q=Jatinder%20Singh%20Bal"> Jatinder Singh Bal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of wireless sensor networks (WSNs) has grown vastly in the last era, pointing out the crucial need for scalable and energy-efficient routing and data gathering and aggregation protocols in corresponding large-scale environments. Wireless Sensor Networks have now recently emerged as a most important computing platform and continue to grow in diverse areas to provide new opportunities for networking and services. However, the energy constrained and limited computing resources of the sensor nodes present major challenges in gathering data. The sensors collect data about their surrounding and forward it to a command centre through a base station. The past few years have witnessed increased interest in the potential use of wireless sensor networks (WSNs) as they are very useful in target detecting and other applications. However, hierarchical clustering protocols have maximum been used in to overall system lifetime, scalability and energy efficiency. In this paper, the state of the art in corresponding hierarchical clustering approaches for large-scale WSN environments is shown. <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=DLCC" title=" DLCC"> DLCC</a>, <a href="https://publications.waset.org/abstracts/search?q=MLCC" title=" MLCC"> MLCC</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20sensor%20networks" title=" wireless sensor networks"> wireless sensor networks</a> </p> <a href="https://publications.waset.org/abstracts/9967/communication-of-sensors-in-clustering-for-wireless-sensor-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9967.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">481</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">1105</span> An Empirical Study to Predict Myocardial Infarction Using K-Means and Hierarchical Clustering </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Md.%20Minhazul%20%20%20Islam">Md. Minhazul Islam</a>, <a href="https://publications.waset.org/abstracts/search?q=Shah%20Ashisul%20Abed%20%20Nipun"> Shah Ashisul Abed Nipun</a>, <a href="https://publications.waset.org/abstracts/search?q=Majharul%20%20Islam"> Majharul Islam</a>, <a href="https://publications.waset.org/abstracts/search?q=Md.%20Abdur%20Rakib%20Rahat"> Md. Abdur Rakib Rahat</a>, <a href="https://publications.waset.org/abstracts/search?q=Jonayet%20Miah"> Jonayet Miah</a>, <a href="https://publications.waset.org/abstracts/search?q=Salsavil%20Kayyum"> Salsavil Kayyum</a>, <a href="https://publications.waset.org/abstracts/search?q=Anwar%20Shadaab"> Anwar Shadaab</a>, <a href="https://publications.waset.org/abstracts/search?q=Faiz%20Al%20Faisal"> Faiz Al Faisal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The target of this research is to predict Myocardial Infarction using unsupervised Machine Learning algorithms. Myocardial Infarction Prediction related to heart disease is a challenging factor faced by doctors & hospitals. In this prediction, accuracy of the heart disease plays a vital role. From this concern, the authors have analyzed on a myocardial dataset to predict myocardial infarction using some popular Machine Learning algorithms K-Means and Hierarchical Clustering. This research includes a collection of data and the classification of data using Machine Learning Algorithms. The authors collected 345 instances along with 26 attributes from different hospitals in Bangladesh. This data have been collected from patients suffering from myocardial infarction along with other symptoms. This model would be able to find and mine hidden facts from historical Myocardial Infarction cases. The aim of this study is to analyze the accuracy level to predict Myocardial Infarction by using Machine Learning techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Machine%20Learning" title="Machine Learning">Machine Learning</a>, <a href="https://publications.waset.org/abstracts/search?q=K-means" title=" K-means"> K-means</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=Myocardial%20Infarction" title=" Myocardial Infarction"> Myocardial Infarction</a>, <a href="https://publications.waset.org/abstracts/search?q=Heart%20Disease" title=" Heart Disease"> Heart Disease</a> </p> <a href="https://publications.waset.org/abstracts/121240/an-empirical-study-to-predict-myocardial-infarction-using-k-means-and-hierarchical-clustering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/121240.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">203</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">1104</span> Detecting of Crime Hot Spots for Crime Mapping</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Somayeh%20Nezami">Somayeh Nezami</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The management of financial and human resources of police in metropolitans requires many information and exact plans to reduce a rate of crime and increase the safety of the society. Geographical Information Systems have an important role in providing crime maps and their analysis. By using them and identification of crime hot spots along with spatial presentation of the results, it is possible to allocate optimum resources while presenting effective methods for decision making and preventive solutions. In this paper, we try to explain and compare between some of the methods of hot spots analysis such as Mode, Fuzzy Mode and Nearest Neighbour Hierarchical spatial clustering (NNH). Then the spots with the highest crime rates of drug smuggling for one province in Iran with borderline with Afghanistan are obtained. We will show that among these three methods NNH leads to the best result. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GIS" title="GIS">GIS</a>, <a href="https://publications.waset.org/abstracts/search?q=Hot%20spots" title=" Hot spots"> Hot spots</a>, <a href="https://publications.waset.org/abstracts/search?q=nearest%20neighbor%20hierarchical%20spatial%20clustering" title=" nearest neighbor hierarchical spatial clustering"> nearest neighbor hierarchical spatial clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=NNH" title=" NNH"> NNH</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20analysis%20of%20crime" title=" spatial analysis of crime"> spatial analysis of crime</a> </p> <a href="https://publications.waset.org/abstracts/68172/detecting-of-crime-hot-spots-for-crime-mapping" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68172.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">329</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">1103</span> A Non-parametric Clustering Approach for Multivariate Geostatistical Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Francky%20Fouedjio">Francky Fouedjio</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multivariate geostatistical data have become omnipresent in the geosciences and pose substantial analysis challenges. One of them is the grouping of data locations into spatially contiguous clusters so that data locations within the same cluster are more similar while clusters are different from each other, in some sense. Spatially contiguous clusters can significantly improve the interpretation that turns the resulting clusters into meaningful geographical subregions. In this paper, we develop an agglomerative hierarchical clustering approach that takes into account the spatial dependency between observations. It relies on a dissimilarity matrix built from a non-parametric kernel estimator of the spatial dependence structure of data. It integrates existing methods to find the optimal cluster number and to evaluate the contribution of variables to the clustering. The capability of the proposed approach to provide spatially compact, connected and meaningful clusters is assessed using bivariate synthetic dataset and multivariate geochemical dataset. The proposed clustering method gives satisfactory results compared to other similar geostatistical 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=geostatistics" title=" geostatistics"> geostatistics</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20data" title=" multivariate data"> multivariate data</a>, <a href="https://publications.waset.org/abstracts/search?q=non-parametric" title=" non-parametric"> non-parametric</a> </p> <a href="https://publications.waset.org/abstracts/46870/a-non-parametric-clustering-approach-for-multivariate-geostatistical-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46870.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">477</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">1102</span> Hybrid Hierarchical Routing Protocol for WSN Lifetime Maximization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20Aoudia">H. Aoudia</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Touati"> Y. Touati</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20H.%20Teguig"> E. H. Teguig</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Ali%20Cherif"> A. Ali Cherif</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Conceiving and developing routing protocols for wireless sensor networks requires considerations on constraints such as network lifetime and energy consumption. In this paper, we propose a hybrid hierarchical routing protocol named HHRP combining both clustering mechanism and multipath optimization taking into account residual energy and RSSI measures. HHRP consists of classifying dynamically nodes into clusters where coordinators nodes with extra privileges are able to manipulate messages, aggregate data and ensure transmission between nodes according to TDMA and CDMA schedules. The reconfiguration of the network is carried out dynamically based on a threshold value which is associated with the number of nodes belonging to the smallest cluster. To show the effectiveness of the proposed approach HHRP, a comparative study with LEACH protocol is illustrated in simulations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=routing%20protocol" title="routing protocol">routing protocol</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=WSN" title=" WSN"> WSN</a> </p> <a href="https://publications.waset.org/abstracts/14550/hybrid-hierarchical-routing-protocol-for-wsn-lifetime-maximization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14550.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">469</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">1101</span> Agglomerative Hierarchical Clustering Based on Morphmetric Parameters of the Populations of Labeo rohita</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fayyaz%20Rasool">Fayyaz Rasool</a>, <a href="https://publications.waset.org/abstracts/search?q=Naureen%20Aziz%20Qureshi"> Naureen Aziz Qureshi</a>, <a href="https://publications.waset.org/abstracts/search?q=Shakeela%20Parveen"> Shakeela Parveen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Labeo rohita populations from five geographical locations from the hatchery and riverine system of Punjab-Pakistan were studied for the clustering on the basis of similarities and differences based on morphometric parameters within the species. Agglomerative Hierarchical Clustering (AHC) was done by using Pearson Correlation Coefficient and Unweighted Pair Group Method with Arithmetic Mean (UPGMA) as Agglomeration method by XLSTAT 2012 version 1.02. A dendrogram with the data on the morphometrics of the representative samples of each site divided the populations of Labeo rohita in to five major clusters or classes. The variance decomposition for the optimal classification values remained as 19.24% for within class variation, while 80.76% for the between class differences. The representative central objects of the each class, the distances between the class centroids and also the distance between the central objects of the classes were generated by the analysis. A measurable distinction between the classes of the populations of the Labeo rohita was indicated in this study which determined the impacts of changing environment and other possible factors influencing the variation level among the populations of the same species. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=AHC" title="AHC">AHC</a>, <a href="https://publications.waset.org/abstracts/search?q=Labeo%20rohita" title=" Labeo rohita"> Labeo rohita</a>, <a href="https://publications.waset.org/abstracts/search?q=hatchery" title=" hatchery"> hatchery</a>, <a href="https://publications.waset.org/abstracts/search?q=riverine" title=" riverine"> riverine</a>, <a href="https://publications.waset.org/abstracts/search?q=morphometric" title=" morphometric "> morphometric </a> </p> <a href="https://publications.waset.org/abstracts/6378/agglomerative-hierarchical-clustering-based-on-morphmetric-parameters-of-the-populations-of-labeo-rohita" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6378.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">456</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">1100</span> Structure Clustering for Milestoning Applications of Complex Conformational Transitions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amani%20Tahat">Amani Tahat</a>, <a href="https://publications.waset.org/abstracts/search?q=Serdal%20Kirmizialtin"> Serdal Kirmizialtin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Trajectory fragment methods such as Markov State Models (MSM), Milestoning (MS) and Transition Path sampling are the prime choice of extending the timescale of all atom Molecular Dynamics simulations. In these approaches, a set of structures that covers the accessible phase space has to be chosen a priori using cluster analysis. Structural clustering serves to partition the conformational state into natural subgroups based on their similarity, an essential statistical methodology that is used for analyzing numerous sets of empirical data produced by Molecular Dynamics (MD) simulations. Local transition kernel among these clusters later used to connect the metastable states using a Markovian kinetic model in MSM and a non-Markovian model in MS. The choice of clustering approach in constructing such kernel is crucial since the high dimensionality of the biomolecular structures might easily confuse the identification of clusters when using the traditional hierarchical clustering methodology. Of particular interest, in the case of MS where the milestones are very close to each other, accurate determination of the milestone identity of the trajectory becomes a challenging issue. Throughout this work we present two cluster analysis methods applied to the cis–trans isomerism of dinucleotide AA. The choice of nucleic acids to commonly used proteins to study the cluster analysis is two fold: i) the energy landscape is rugged; hence transitions are more complex, enabling a more realistic model to study conformational transitions, ii) Nucleic acids conformational space is high dimensional. A diverse set of internal coordinates is necessary to describe the metastable states in nucleic acids, posing a challenge in studying the conformational transitions. Herein, we need improved clustering methods that accurately identify the AA structure in its metastable states in a robust way for a wide range of confused data conditions. The single linkage approach of the hierarchical clustering available in GROMACS MD-package is the first clustering methodology applied to our data. Self Organizing Map (SOM) neural network, that also known as a Kohonen network, is the second data clustering methodology. The performance comparison of the neural network as well as hierarchical clustering method is studied by means of computing the mean first passage times for the cis-trans conformational rates. Our hope is that this study provides insight into the complexities and need in determining the appropriate clustering algorithm for kinetic analysis. Our results can improve the effectiveness of decisions based on clustering confused empirical data in studying conformational transitions in biomolecules. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=milestoning" title="milestoning">milestoning</a>, <a href="https://publications.waset.org/abstracts/search?q=self%20organizing%20map" title=" self organizing map"> self organizing map</a>, <a href="https://publications.waset.org/abstracts/search?q=single%20linkage" title=" single linkage"> single linkage</a>, <a href="https://publications.waset.org/abstracts/search?q=structure%20clustering" title=" structure clustering "> structure clustering </a> </p> <a href="https://publications.waset.org/abstracts/82477/structure-clustering-for-milestoning-applications-of-complex-conformational-transitions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82477.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">224</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">1099</span> Unseen Classes: The Paradigm Shift in Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vani%20Singhal">Vani Singhal</a>, <a href="https://publications.waset.org/abstracts/search?q=Jitendra%20Parmar"> Jitendra Parmar</a>, <a href="https://publications.waset.org/abstracts/search?q=Satyendra%20Singh%20Chouhan"> Satyendra Singh Chouhan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Unseen class discovery has now become an important part of a machine-learning algorithm to judge new classes. Unseen classes are the classes on which the machine learning model is not trained on. With the advancement in technology and AI replacing humans, the amount of data has increased to the next level. So while implementing a model on real-world examples, we come across unseen new classes. Our aim is to find the number of unseen classes by using a hierarchical-based active learning algorithm. The algorithm is based on hierarchical clustering as well as active sampling. The number of clusters that we will get in the end will give the number of unseen classes. The total clusters will also contain some clusters that have unseen classes. Instead of first discovering unseen classes and then finding their number, we directly calculated the number by applying the algorithm. The dataset used is for intent classification. The target data is the intent of the corresponding query. We conclude that when the machine learning model will encounter real-world data, it will automatically find the number of unseen classes. In the future, our next work would be to label these unseen classes correctly. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=active%20sampling" title="active sampling">active sampling</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=open%20world%20learning" title=" open world learning"> open world learning</a>, <a href="https://publications.waset.org/abstracts/search?q=unseen%20class%20discovery" title=" unseen class discovery"> unseen class discovery</a> </p> <a href="https://publications.waset.org/abstracts/137110/unseen-classes-the-paradigm-shift-in-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137110.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">172</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">1098</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">1097</span> Power Iteration Clustering Based on Deflation Technique on Large Scale Graphs </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Taysir%20Soliman">Taysir Soliman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the current popular clustering techniques is Spectral Clustering (SC) because of its advantages over conventional approaches such as hierarchical clustering, k-means, etc. and other techniques as well. However, one of the disadvantages of SC is the time consuming process because it requires computing the eigenvectors. In the past to overcome this disadvantage, a number of attempts have been proposed such as the Power Iteration Clustering (PIC) technique, which is one of versions from SC; some of PIC advantages are: 1) its scalability and efficiency, 2) finding one pseudo-eigenvectors instead of computing eigenvectors, and 3) linear combination of the eigenvectors in linear time. However, its worst disadvantage is an inter-class collision problem because it used only one pseudo-eigenvectors which is not enough. Previous researchers developed Deflation-based Power Iteration Clustering (DPIC) to overcome problems of PIC technique on inter-class collision with the same efficiency of PIC. In this paper, we developed Parallel DPIC (PDPIC) to improve the time and memory complexity which is run on apache spark framework using sparse matrix. To test the performance of PDPIC, we compared it to SC, ESCG, ESCALG algorithms on four small graph benchmark datasets and nine large graph benchmark datasets, where PDPIC proved higher accuracy and better time consuming than other compared algorithms. <p class="card-text"><strong>Keywords:</strong> <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=power%20iteration%20clustering" title=" power iteration clustering"> power iteration clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=deflation-based%20power%20iteration%20clustering" title=" deflation-based power iteration clustering"> deflation-based power iteration clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=Apache%20spark" title=" Apache spark"> Apache spark</a>, <a href="https://publications.waset.org/abstracts/search?q=large%20graph" title=" large graph"> large graph</a> </p> <a href="https://publications.waset.org/abstracts/120360/power-iteration-clustering-based-on-deflation-technique-on-large-scale-graphs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/120360.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">1096</span> Dissimilarity Measure for General Histogram Data and Its Application to Hierarchical Clustering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Umbleja">K. Umbleja</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Ichino"> M. Ichino</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Symbolic data mining has been developed to analyze data in very large datasets. It is also useful in cases when entry specific details should remain hidden. Symbolic data mining is quickly gaining popularity as datasets in need of analyzing are becoming ever larger. One type of such symbolic data is a histogram, which enables to save huge amounts of information into a single variable with high-level of granularity. Other types of symbolic data can also be described in histograms, therefore making histogram a very important and general symbolic data type - a method developed for histograms - can also be applied to other types of symbolic data. Due to its complex structure, analyzing histograms is complicated. This paper proposes a method, which allows to compare two histogram-valued variables and therefore find a dissimilarity between two histograms. Proposed method uses the Ichino-Yaguchi dissimilarity measure for mixed feature-type data analysis as a base and develops a dissimilarity measure specifically for histogram data, which allows to compare histograms with different number of bins and bin widths (so called general histogram). Proposed dissimilarity measure is then used as a measure for clustering. Furthermore, linkage method based on weighted averages is proposed with the concept of cluster compactness to measure the quality of clustering. The method is then validated with application on real datasets. As a result, the proposed dissimilarity measure is found producing adequate and comparable results with general histograms without the loss of detail or need to transform the data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dissimilarity%20measure" title="dissimilarity measure">dissimilarity measure</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=histograms" title=" histograms"> histograms</a>, <a href="https://publications.waset.org/abstracts/search?q=symbolic%20data%20analysis" title=" symbolic data analysis"> symbolic data analysis</a> </p> <a href="https://publications.waset.org/abstracts/92018/dissimilarity-measure-for-general-histogram-data-and-its-application-to-hierarchical-clustering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92018.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">162</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">1095</span> A Polynomial Time Clustering Algorithm for Solving the Assignment Problem in the Vehicle Routing Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lydia%20Wahid">Lydia Wahid</a>, <a href="https://publications.waset.org/abstracts/search?q=Mona%20F.%20Ahmed"> Mona F. Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Nevin%20Darwish"> Nevin Darwish</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The vehicle routing problem (VRP) consists of a group of customers that needs to be served. Each customer has a certain demand of goods. A central depot having a fleet of vehicles is responsible for supplying the customers with their demands. The problem is composed of two subproblems: The first subproblem is an assignment problem where the number of vehicles that will be used as well as the customers assigned to each vehicle are determined. The second subproblem is the routing problem in which for each vehicle having a number of customers assigned to it, the order of visits of the customers is determined. Optimal number of vehicles, as well as optimal total distance, should be achieved. In this paper, an approach for solving the first subproblem (the assignment problem) is presented. In the approach, a clustering algorithm is proposed for finding the optimal number of vehicles by grouping the customers into clusters where each cluster is visited by one vehicle. Finding the optimal number of clusters is NP-hard. This work presents a polynomial time clustering algorithm for finding the optimal number of clusters and solving the assignment problem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=vehicle%20routing%20problems" title="vehicle routing problems">vehicle routing problems</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=Clarke%20and%20Wright%20Saving%20Method" title=" Clarke and Wright Saving Method"> Clarke and Wright Saving Method</a>, <a href="https://publications.waset.org/abstracts/search?q=agglomerative%20hierarchical%20clustering" title=" agglomerative hierarchical clustering"> agglomerative hierarchical clustering</a> </p> <a href="https://publications.waset.org/abstracts/85552/a-polynomial-time-clustering-algorithm-for-solving-the-assignment-problem-in-the-vehicle-routing-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85552.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">393</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">1094</span> Efficient Subgoal Discovery for Hierarchical Reinforcement Learning Using Local Computations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adrian%20Millea">Adrian Millea</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In hierarchical reinforcement learning, one of the main issues encountered is the discovery of subgoal states or options (which are policies reaching subgoal states) by partitioning the environment in a meaningful way. This partitioning usually requires an expensive global clustering operation or eigendecomposition of the Laplacian of the states graph. We propose a local solution to this issue, much more efficient than algorithms using global information, which successfully discovers subgoal states by computing a simple function, which we call heterogeneity for each state as a function of its neighbors. Moreover, we construct a value function using the difference in heterogeneity from one step to the next, as reward, such that we are able to explore the state space much more efficiently than say epsilon-greedy. The same principle can then be applied to higher level of the hierarchy, where now states are subgoals discovered at the level below. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=exploration" title="exploration">exploration</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20reinforcement%20learning" title=" hierarchical reinforcement learning"> hierarchical reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=locality" title=" locality"> locality</a>, <a href="https://publications.waset.org/abstracts/search?q=options" title=" options"> options</a>, <a href="https://publications.waset.org/abstracts/search?q=value%20functions" title=" value functions"> value functions</a> </p> <a href="https://publications.waset.org/abstracts/134077/efficient-subgoal-discovery-for-hierarchical-reinforcement-learning-using-local-computations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/134077.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">171</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">1093</span> A Comparative Analysis of Clustering Approaches for Understanding Patterns in Health Insurance Uptake: Evidence from Sociodemographic Kenyan Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nelson%20Kimeli%20Kemboi%20Yego">Nelson Kimeli Kemboi Yego</a>, <a href="https://publications.waset.org/abstracts/search?q=Juma%20Kasozi"> Juma Kasozi</a>, <a href="https://publications.waset.org/abstracts/search?q=Joseph%20Nkruzinza"> Joseph Nkruzinza</a>, <a href="https://publications.waset.org/abstracts/search?q=Francis%20Kipkogei"> Francis Kipkogei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study investigated the low uptake of health insurance in Kenya despite efforts to achieve universal health coverage through various health insurance schemes. Unsupervised machine learning techniques were employed to identify patterns in health insurance uptake based on sociodemographic factors among Kenyan households. The aim was to identify key demographic groups that are underinsured and to provide insights for the development of effective policies and outreach programs. Using the 2021 FinAccess Survey, the study clustered Kenyan households based on their health insurance uptake and sociodemographic features to reveal patterns in health insurance uptake across the country. The effectiveness of k-prototypes clustering, hierarchical clustering, and agglomerative hierarchical clustering in clustering based on sociodemographic factors was compared. The k-prototypes approach was found to be the most effective at uncovering distinct and well-separated clusters in the Kenyan sociodemographic data related to health insurance uptake based on silhouette, Calinski-Harabasz, Davies-Bouldin, and Rand indices. Hence, it was utilized in uncovering the patterns in uptake. The results of the analysis indicate that inclusivity in health insurance is greatly related to affordability. The findings suggest that targeted policy interventions and outreach programs are necessary to increase health insurance uptake in Kenya, with the ultimate goal of achieving universal health coverage. The study provides important insights for policymakers and stakeholders in the health insurance sector to address the low uptake of health insurance and to ensure that healthcare services are accessible and affordable to all Kenyans, regardless of their socio-demographic status. The study highlights the potential of unsupervised machine learning techniques to provide insights into complex health policy issues and improve decision-making in the health sector. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=health%20insurance" title="health insurance">health insurance</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=clustering%20algorithms" title=" clustering algorithms"> clustering algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/164178/a-comparative-analysis-of-clustering-approaches-for-understanding-patterns-in-health-insurance-uptake-evidence-from-sociodemographic-kenyan-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164178.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">138</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">1092</span> Energy Efficient Clustering with Reliable and Load-Balanced Multipath Routing for Wireless Sensor Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alamgir%20Naushad">Alamgir Naushad</a>, <a href="https://publications.waset.org/abstracts/search?q=Ghulam%20Abbas"> Ghulam Abbas</a>, <a href="https://publications.waset.org/abstracts/search?q=Shehzad%20Ali%20Shah"> Shehzad Ali Shah</a>, <a href="https://publications.waset.org/abstracts/search?q=Ziaul%20Haq%20Abbas"> Ziaul Haq Abbas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Unlike conventional networks, it is particularly challenging to manage resources efficiently in Wireless Sensor Networks (WSNs) due to their inherent characteristics, such as dynamic network topology and limited bandwidth and battery power. To ensure energy efficiency, this paper presents a routing protocol for WSNs, namely, Enhanced Hybrid Multipath Routing (EHMR), which employs hierarchical clustering and proposes a next hop selection mechanism between nodes according to a maximum residual energy metric together with a minimum hop count. Load-balancing of data traffic over multiple paths is achieved for a better packet delivery ratio and low latency rate. Reliability is ensured in terms of higher data rate and lower end-to-end delay. EHMR also enhances the fast-failure recovery mechanism to recover a failed path. Simulation results demonstrate that EHMR achieves a higher packet delivery ratio, reduced energy consumption per-packet delivery, lower end-to-end latency, and reduced effect of data rate on packet delivery ratio when compared with eminent WSN routing protocols. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=energy%20efficiency" title="energy efficiency">energy efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=load-balancing" title=" load-balancing"> load-balancing</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=multipath%20routing" title=" multipath routing"> multipath routing</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20sensor%20networks" title=" wireless sensor networks"> wireless sensor networks</a> </p> <a href="https://publications.waset.org/abstracts/169528/energy-efficient-clustering-with-reliable-and-load-balanced-multipath-routing-for-wireless-sensor-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169528.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">85</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">1091</span> Identifying Autism Spectrum Disorder Using Optimization-Based Clustering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sharifah%20Mousli">Sharifah Mousli</a>, <a href="https://publications.waset.org/abstracts/search?q=Sona%20Taheri"> Sona Taheri</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiayuan%20He"> Jiayuan He</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Autism spectrum disorder (ASD) is a complex developmental condition involving persistent difficulties with social communication, restricted interests, and repetitive behavior. The challenges associated with ASD can interfere with an affected individual’s ability to function in social, academic, and employment settings. Although there is no effective medication known to treat ASD, to our best knowledge, early intervention can significantly improve an affected individual’s overall development. Hence, an accurate diagnosis of ASD at an early phase is essential. The use of machine learning approaches improves and speeds up the diagnosis of ASD. In this paper, we focus on the application of unsupervised clustering methods in ASD as a large volume of ASD data generated through hospitals, therapy centers, and mobile applications has no pre-existing labels. We conduct a comparative analysis using seven clustering approaches such as K-means, agglomerative hierarchical, model-based, fuzzy-C-means, affinity propagation, self organizing maps, linear vector quantisation – as well as the recently developed optimization-based clustering (COMSEP-Clust) approach. We evaluate the performances of the clustering methods extensively on real-world ASD datasets encompassing different age groups: toddlers, children, adolescents, and adults. Our experimental results suggest that the COMSEP-Clust approach outperforms the other seven methods in recognizing ASD with well-separated clusters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autism%20spectrum%20disorder" title="autism spectrum disorder">autism spectrum disorder</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20machine%20learning" title=" unsupervised machine learning"> unsupervised machine learning</a> </p> <a href="https://publications.waset.org/abstracts/173901/identifying-autism-spectrum-disorder-using-optimization-based-clustering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173901.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">116</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">1090</span> Meta-Learning for Hierarchical Classification and Applications in Bioinformatics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fabio%20Fabris">Fabio Fabris</a>, <a href="https://publications.waset.org/abstracts/search?q=Alex%20A.%20Freitas"> Alex A. Freitas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hierarchical classification is a special type of classification task where the class labels are organised into a hierarchy, with more generic class labels being ancestors of more specific ones. Meta-learning for classification-algorithm recommendation consists of recommending to the user a classification algorithm, from a pool of candidate algorithms, for a dataset, based on the past performance of the candidate algorithms in other datasets. Meta-learning is normally used in conventional, non-hierarchical classification. By contrast, this paper proposes a meta-learning approach for more challenging task of hierarchical classification, and evaluates it in a large number of bioinformatics datasets. Hierarchical classification is especially relevant for bioinformatics problems, as protein and gene functions tend to be organised into a hierarchy of class labels. This work proposes meta-learning approach for recommending the best hierarchical classification algorithm to a hierarchical classification dataset. This work’s contributions are: 1) proposing an algorithm for splitting hierarchical datasets into new datasets to increase the number of meta-instances, 2) proposing meta-features for hierarchical classification, and 3) interpreting decision-tree meta-models for hierarchical classification algorithm recommendation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=algorithm%20recommendation" title="algorithm recommendation">algorithm recommendation</a>, <a href="https://publications.waset.org/abstracts/search?q=meta-learning" title=" meta-learning"> meta-learning</a>, <a href="https://publications.waset.org/abstracts/search?q=bioinformatics" title=" bioinformatics"> bioinformatics</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20classification" title=" hierarchical classification"> hierarchical classification</a> </p> <a href="https://publications.waset.org/abstracts/81005/meta-learning-for-hierarchical-classification-and-applications-in-bioinformatics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81005.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">314</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">1089</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">222</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">1088</span> Analysis of Expression Data Using Unsupervised Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20A.%20I%20Perera">M. A. I Perera</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20R.%20Wijesinghe"> C. R. Wijesinghe</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20R.%20%20Weerasinghe"> A. R. Weerasinghe</a> </p> <p class="card-text"><strong>Abstract:</strong></p> his study was conducted to review and identify the unsupervised techniques that can be employed to analyze gene expression data in order to identify better subtypes of tumors. Identifying subtypes of cancer help in improving the efficacy and reducing the toxicity of the treatments by identifying clues to find target therapeutics. Process of gene expression data analysis described under three steps as preprocessing, clustering, and cluster validation. Feature selection is important since the genomic data are high dimensional with a large number of features compared to samples. Hierarchical clustering and K Means are often used in the analysis of gene expression data. There are several cluster validation techniques used in validating the clusters. Heatmaps are an effective external validation method that allows comparing the identified classes with clinical variables and visual analysis of the classes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cancer%20subtypes" title="cancer subtypes">cancer subtypes</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20expression%20data%20analysis" title=" gene expression data analysis"> gene expression data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=cluster%20validation" title=" cluster validation"> cluster validation</a> </p> <a href="https://publications.waset.org/abstracts/129027/analysis-of-expression-data-using-unsupervised-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129027.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">149</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">1087</span> Identification of Biological Pathways Causative for Breast Cancer Using Unsupervised Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Karthik%20Mittal">Karthik Mittal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study performs an unsupervised machine learning analysis to find clusters of related SNPs which highlight biological pathways that are important for the biological mechanisms of breast cancer. Studying genetic variations in isolation is illogical because these genetic variations are known to modulate protein production and function; the downstream effects of these modifications on biological outcomes are highly interconnected. After extracting the SNPs and their effect on different types of breast cancer using the MRBase library, two unsupervised machine learning clustering algorithms were implemented on the genetic variants: a k-means clustering algorithm and a hierarchical clustering algorithm; furthermore, principal component analysis was executed to visually represent the data. These algorithms specifically used the SNP’s beta value on the three different types of breast cancer tested in this project (estrogen-receptor positive breast cancer, estrogen-receptor negative breast cancer, and breast cancer in general) to perform this clustering. Two significant genetic pathways validated the clustering produced by this project: the MAPK signaling pathway and the connection between the BRCA2 gene and the ESR1 gene. This study provides the first proof of concept showing the importance of unsupervised machine learning in interpreting GWAS summary statistics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title="breast cancer">breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20biology" title=" computational biology"> computational biology</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20machine%20learning" title=" unsupervised machine learning"> unsupervised machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=k-means" title=" k-means"> k-means</a>, <a href="https://publications.waset.org/abstracts/search?q=PCA" title=" PCA"> PCA</a> </p> <a href="https://publications.waset.org/abstracts/148748/identification-of-biological-pathways-causative-for-breast-cancer-using-unsupervised-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148748.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">146</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=hierarchical%20clustering&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=hierarchical%20clustering&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=hierarchical%20clustering&page=4">4</a></li> <li class="page-item"><a class="page-link" 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