{"id":8271,"date":"2020-03-27T09:08:29","date_gmt":"2020-03-27T08:08:29","guid":{"rendered":"https:\/\/complex-systems-ai.com\/?page_id=8271"},"modified":"2024-02-25T18:32:30","modified_gmt":"2024-02-25T17:32:30","slug":"partitionnement-de-donnees","status":"publish","type":"page","link":"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/","title":{"rendered":"Data Partitioning \/ Clustering 101"},"content":{"rendered":"<div data-elementor-type=\"wp-page\" data-elementor-id=\"8271\" class=\"elementor elementor-8271\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-58c5311 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"58c5311\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-7429d9f\" data-id=\"7429d9f\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-af4f296 elementor-align-justify elementor-widget elementor-widget-button\" data-id=\"af4f296\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/complex-systems-ai.com\/en\/2020\/04\/03\/theories-and-algorithms-2\/\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Theories<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-387abe0\" data-id=\"387abe0\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-bdb673c elementor-align-justify elementor-widget elementor-widget-button\" data-id=\"bdb673c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/complex-systems-ai.com\/en\/\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Home page<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-a9873c3\" data-id=\"a9873c3\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d35bcae elementor-align-justify elementor-widget elementor-widget-button\" data-id=\"d35bcae\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/fr.wikipedia.org\/wiki\/Partitionnement_de_donn%C3%A9es\" target=\"_blank\" rel=\"noopener\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Wiki<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-a1de6b2 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"a1de6b2\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-277764f\" data-id=\"277764f\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-1b48162 elementor-widget elementor-widget-toggle\" data-id=\"1b48162\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"toggle.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-toggle\">\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-2861\" class=\"elementor-tab-title\" data-tab=\"1\" role=\"button\" aria-controls=\"elementor-tab-content-2861\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-left\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><i class=\"fas fa-caret-right\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><i class=\"elementor-toggle-icon-opened fas fa-caret-up\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">I. Distance measurement and Similarity function (data partitioning)<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-2861\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"1\" role=\"region\" aria-labelledby=\"elementor-tab-title-2861\"><ul>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/minkowski-for-numeric-attributes\/\">Minkowski for numeric attributes<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/distance-measures-for-binary-attributes\/\">For binary attributes<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/distance-measures-for-nominal-attributes\/\">For nominal attributes<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/distance-measures-for-ordinal-attributes\/\">For ordinal attributes<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/distance-measures-for-attributes-of-mixed-type\/\">For attributes of mixed type<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/similarity-function\/\">Similarity function<\/a><\/li>\n<\/ul><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-2862\" class=\"elementor-tab-title\" data-tab=\"2\" role=\"button\" aria-controls=\"elementor-tab-content-2862\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-left\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><i class=\"fas fa-caret-right\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><i class=\"elementor-toggle-icon-opened fas fa-caret-up\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">II. Evaluation criteria<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-2862\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"2\" role=\"region\" aria-labelledby=\"elementor-tab-title-2862\"><ul>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/hopkins-statistic\/\">Data quality<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">Internal quality<\/a><\/li>\n<\/ul>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">Sum of the quadratic error<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">Dispersion criteria<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">Category utility metric<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">Cutting measures<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">Ball Hall<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">Banfeld-Raftery<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">Condorcet criterion<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">Criterion C<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">Calinski-Harabasz<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">Davies-Bouldin<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">Det_Ratio<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">Dunn<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">GDImn<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">Gamma<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">G+<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">Ksq_DetW<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">Log_Det_Ratio<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">Log_SS_Ratio<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">McClain-Rao<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">PBM<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">Biserial point<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">Ratkawsky-Lance<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">Ray-Turi<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">Scott Symons<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">SD_Scat<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">SD_Dis<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">S_Dbw<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">Silhouette<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">Trace W<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">WiB trace<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">Wemmert-Gan\u00e7arski<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/internal-quality-criteria\/\">Xie-Beni<\/a><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<ul>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/external-quality-criteria\/\">External quality<\/a>\n<ul>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/external-quality-criteria\/\">Precision recall measurement<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/external-quality-criteria\/\">Indicator variables<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/external-quality-criteria\/\">Measure based on mutual information<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/external-quality-criteria\/\">Entropy, purity and V-measure<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/external-quality-criteria\/\">Czekanowski-Dice<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/external-quality-criteria\/\">Folkes-Mallows<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/external-quality-criteria\/\">Hubert \u0393<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/external-quality-criteria\/\">Jaccard<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/external-quality-criteria\/\">Kulczynski<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/external-quality-criteria\/\">McNemar<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/external-quality-criteria\/\">Phi<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/external-quality-criteria\/\">Rand<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/external-quality-criteria\/\">Rogers-Tanimoto<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/external-quality-criteria\/\">Russell Rao<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/external-quality-criteria\/\">Sokal-Sneath<\/a><\/li>\n<\/ul>\n<\/li>\n<li>Relative quality\n<ul>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/quality-over-number-of-clusters\/\">Quality on the number of clusters<\/a><\/li>\n<li><a href=\"https:\/\/people.eecs.berkeley.edu\/~jordan\/sail\/readings\/luxburg_ftml.pdf\" target=\"_blank\" rel=\"noopener\">Cluster stability<\/a><\/li>\n<\/ul>\n<\/li>\n<li><a href=\"https:\/\/cran.r-project.org\/web\/packages\/clusterCrit\/vignettes\/clusterCrit.pdf\" target=\"_blank\" rel=\"noopener\">The clusterCrit package<\/a><\/li>\n<li><a href=\"https:\/\/nbviewer.org\/github\/bejar\/AMLTNotebooks\/blob\/master\/Notebooks\/10ClusterValidation.ipynb\" target=\"_blank\" rel=\"noopener\">Notebooks on cluster validation<\/a><\/li>\n<\/ul><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-2863\" class=\"elementor-tab-title\" data-tab=\"3\" role=\"button\" aria-controls=\"elementor-tab-content-2863\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-left\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><i class=\"fas fa-caret-right\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><i class=\"elementor-toggle-icon-opened fas fa-caret-up\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">III. Hierarchical algorithms<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-2863\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"3\" role=\"region\" aria-labelledby=\"elementor-tab-title-2863\"><ul>\n<li>Hierarchical agglomerative\n<ul>\n<li><a href=\"https:\/\/towardsdatascience.com\/introduction-hierarchical-clustering-d3066c6b560e\" target=\"_blank\" rel=\"noopener\">Linkage (single, complete, average)<\/a><\/li>\n<li><a href=\"https:\/\/www.researchgate.net\/publication\/51962445_Ward&#039;s_Hierarchical_Clustering_Method_Clustering_Criterion_andAgglomerative_Algorithm\" target=\"_blank\" rel=\"noopener\">Ward&#039;s method<\/a><\/li>\n<li><a href=\"https:\/\/www2.cs.sfu.ca\/CourseCentral\/459\/han\/papers\/zhang96.pdf\" target=\"_blank\" rel=\"noopener\">Balanced iterative reducing and clusteringusing hierarchies (BIRCH)<\/a><\/li>\n<li><a href=\"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0306437901000084\" target=\"_blank\" rel=\"noopener\">Clustering using re-resentatives (CURE)<\/a><\/li>\n<li><a href=\"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0306437900000223\" target=\"_blank\" rel=\"noopener\">Robust clustering using links (ROCK)<\/a><\/li>\n<\/ul>\n<\/li>\n<li>Hierarchical divisive\n<ul>\n<li><a href=\"https:\/\/books.google.fr\/books?hl=fr&amp;lr=&amp;id=YeFQHiikNo0C&amp;oi=fnd&amp;pg=PR11&amp;dq=Finding+Groups+in+Data:+An+Introduction+To+Cluster+Analysis&amp;ots=5AvdB9QEsy&amp;sig=ood4e7rDOFL5wZZAacKe7TT1FSM\" target=\"_blank\" rel=\"noopener\">Divisive analysis (DIANA)<\/a><\/li>\n<li><a href=\"https:\/\/books.google.fr\/books?hl=fr&amp;lr=&amp;id=YeFQHiikNo0C&amp;oi=fnd&amp;pg=PR11&amp;dq=Finding+Groups+in+Data:+An+Introduction+To+Cluster+Analysis&amp;ots=5AvdB9QEsy&amp;sig=ood4e7rDOFL5wZZAacKe7TT1FSM\" target=\"_blank\" rel=\"noopener\">Monothetic analysis (MONA)<\/a><\/li>\n<\/ul>\n<\/li>\n<li>Relative hierarchical\n<ul>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/895974\/\" target=\"_blank\" rel=\"noopener\">Scale-space clustering<\/a><\/li>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/1019208\/\" target=\"_blank\" rel=\"noopener\">Similarity-based agglomerative clustering (SBAC)<\/a><\/li>\n<\/ul>\n<\/li>\n<\/ul><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-2864\" class=\"elementor-tab-title\" data-tab=\"4\" role=\"button\" aria-controls=\"elementor-tab-content-2864\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-left\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><i class=\"fas fa-caret-right\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><i class=\"elementor-toggle-icon-opened fas fa-caret-up\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">IV. Estimation algorithms<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-2864\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"4\" role=\"region\" aria-labelledby=\"elementor-tab-title-2864\"><ul>\n<li>Squared error-based\n<ul>\n<li><a href=\"https:\/\/link.springer.com\/chapter\/10.1007\/3-540-48311-X_205?error=cookies_not_supported&#038;code=cad79c59-1fa3-48bf-aa12-95187d6c01ca\" target=\"_blank\" rel=\"noopener\">K-means<\/a><\/li>\n<li><a href=\"https:\/\/link.springer.com\/chapter\/10.1007\/3-540-44533-1_24?error=cookies_not_supported&#038;code=64963d87-c4fe-4d99-b36d-10e00852072d\" target=\"_blank\" rel=\"noopener\">K-medoid<\/a><\/li>\n<li><a href=\"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/bs.3830120210\" target=\"_blank\" rel=\"noopener\">Iterative self-organizing technical data analysis (ISODATA)<\/a><\/li>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/764879\/\" target=\"_blank\" rel=\"noopener\">Genetic-means algorithm (GKA)<\/a><\/li>\n<li><a href=\"https:\/\/books.google.fr\/books?hl=fr&amp;lr=&amp;id=YeFQHiikNo0C&amp;oi=fnd&amp;pg=PR11&amp;dq=Finding+Groups+in+Data:+An+Introduction+To+Cluster+Analysis&amp;ots=5AvdB9QGzC&amp;sig=fKXnrF2AKXHhEXIzcynN6lUbhNU\" target=\"_blank\" rel=\"noopener\">Partitioning around medoids (PAM)<\/a><\/li>\n<li><a href=\"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0893608001001046\" target=\"_blank\" rel=\"noopener\">Enhanced LBG<\/a><\/li>\n<\/ul>\n<\/li>\n<li>pdf estimate via mixture densities\n<ul>\n<li><a href=\"https:\/\/cds.cern.ch\/record\/683166\/files\/0471056693_TOC.pdf\" target=\"_blank\" rel=\"noopener\">Maximum likelihood<\/a><\/li>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/535841\/\" target=\"_blank\" rel=\"noopener\">Gaussian mixture density decomposition (GMDD)<\/a><\/li>\n<\/ul>\n<ul>\n<li><a href=\"https:\/\/www.semanticscholar.org\/paper\/Bayesian-Classification-%28AutoClass%29%3A-Theory-and-Cheeseman-Stutz\/42f75b297aed474599c8e598dd211a1999804138?p2df\" target=\"_blank\" rel=\"noopener\">AutoClass<\/a><\/li>\n<li><a href=\"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/1467-842X.00071\" target=\"_blank\" rel=\"noopener\">Multimix<\/a><\/li>\n<li><a href=\"https:\/\/people.smp.uq.edu.au\/GeoffMcLachlan\/emmix\/paper.pdf\" target=\"_blank\" rel=\"noopener\">EM based mixture program (EMMIX)<\/a><\/li>\n<li><a href=\"https:\/\/www.semanticscholar.org\/paper\/Intrinsic-classification-by-MML-the-Snob-program-Wallace-Dowe\/216148fae2cd84aeed2f76f9af577dc87ca83aa7?p2df\" target=\"_blank\" rel=\"noopener\">Snob<\/a><\/li>\n<\/ul>\n<\/li>\n<\/ul><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-2865\" class=\"elementor-tab-title\" data-tab=\"5\" role=\"button\" aria-controls=\"elementor-tab-content-2865\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-left\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><i class=\"fas fa-caret-right\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><i class=\"elementor-toggle-icon-opened fas fa-caret-up\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">V. Graph or combinatorial algorithms<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-2865\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"5\" role=\"region\" aria-labelledby=\"elementor-tab-title-2865\"><ul>\n<li>Graph theory-based\n<ul>\n<li><a href=\"https:\/\/www-users.cse.umn.edu\/~hanxx023\/dmclass\/chameleon.pdf\" target=\"_blank\" rel=\"noopener\">Chameleon<\/a><\/li>\n<li><a href=\"https:\/\/vladestivill-castro.net\/teaching\/kdd.d\/readings.d\/tr99-05.pdf\" target=\"_blank\" rel=\"noopener\">AMOEBA<\/a><\/li>\n<\/ul>\n<ul>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/989504\" target=\"_blank\" rel=\"noopener\">Delaunay triangulation graph (DTG)<\/a><\/li>\n<li><a href=\"https:\/\/www.researchgate.net\/publication\/222648006_A_Clustering_Algorithm_Based_on_Graph_Connectivity\" target=\"_blank\" rel=\"noopener\">Highly connected subgraphs (HCS)<\/a><\/li>\n<li><a href=\"https:\/\/www.aaai.org\/Papers\/ISMB\/2000\/ISMB00-032.pdf\" target=\"_blank\" rel=\"noopener\">Clustering identification via connectivity kernels (CLICK)<\/a><\/li>\n<li><a href=\"http:\/\/citeseerx.ist.psu.edu\/viewdoc\/download?doi=10.1.1.34.5341&amp;rep=rep1&amp;type=pdf\" target=\"_blank\" rel=\"noopener\">Cluster affinity search technique (CAST)<\/a><\/li>\n<\/ul>\n<\/li>\n<li>Combinatorial search techniques-based\n<ul>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/771164\/\" target=\"_blank\" rel=\"noopener\">Genetically guided algorithm (GGA)<\/a><\/li>\n<li><a href=\"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320300000054\" target=\"_blank\" rel=\"noopener\">CLUSTERING<\/a><\/li>\n<li><a href=\"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/003132039500022R\" target=\"_blank\" rel=\"noopener\">Tabu search clustering<\/a><\/li>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/616356\/\" target=\"_blank\" rel=\"noopener\">Fuzzy Tabu search clustering<\/a><\/li>\n<li><a href=\"http:\/\/www.derczynski.com\/papers\/archive\/1-s2.0-003132039290088Z-main.pdf\" target=\"_blank\" rel=\"noopener\">Simulated annealing to clustering<\/a><\/li>\n<\/ul>\n<\/li>\n<\/ul><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-2866\" class=\"elementor-tab-title\" data-tab=\"6\" role=\"button\" aria-controls=\"elementor-tab-content-2866\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-left\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><i class=\"fas fa-caret-right\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><i class=\"elementor-toggle-icon-opened fas fa-caret-up\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">VI. Fuzzy algorithms<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-2866\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"6\" role=\"region\" aria-labelledby=\"elementor-tab-title-2866\"><ul>\n<li><a href=\"https:\/\/www.wiley.com\/en-us\/Fuzzy+Cluster+Analysis%3A+Methods+for+Classification%2C+Data+Analysis+and+Image+Recognition-p-9780471988649\" target=\"_blank\" rel=\"noopener\">Fuzzy c-means (FCM)<\/a><\/li>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/299710\/\" target=\"_blank\" rel=\"noopener\">Mountain method (MM)<\/a><\/li>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/227387\/\" target=\"_blank\" rel=\"noopener\">Possibilistic c-means clustering algorithm (PCM)<\/a><\/li>\n<li><a href=\"https:\/\/www.wiley.com\/en-us\/Fuzzy+Cluster+Analysis%3A+Methods+for+Classification%2C+Data+Analysis+and+Image+Recognition-p-9780471988649\" target=\"_blank\" rel=\"noopener\">Fuzzy c-cluster shapes<\/a><\/li>\n<\/ul><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-2867\" class=\"elementor-tab-title\" data-tab=\"7\" role=\"button\" aria-controls=\"elementor-tab-content-2867\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-left\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><i class=\"fas fa-caret-right\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><i class=\"elementor-toggle-icon-opened fas fa-caret-up\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">VII. Neural network algorithms<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-2867\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"7\" role=\"region\" aria-labelledby=\"elementor-tab-title-2867\"><ul>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/536304\/\" target=\"_blank\" rel=\"noopener\">Generalized Learning vector quantization F (GLVQ-F)<\/a><\/li>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/846743\/\" target=\"_blank\" rel=\"noopener\">Self-organizing feature map (SOFM)<\/a><\/li>\n<li><a href=\"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/089360809190012T\" target=\"_blank\" rel=\"noopener\">ARTMAP<\/a><\/li>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/159059\/\" target=\"_blank\" rel=\"noopener\">Fuzzy ARTMAP<\/a><\/li>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/182691\/\" target=\"_blank\" rel=\"noopener\">THE PART<\/a><\/li>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/1000130\/\" target=\"_blank\" rel=\"noopener\">SART<\/a><\/li>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/712183\/\" target=\"_blank\" rel=\"noopener\">Cluster detection and labeling network (CDL)<\/a><\/li>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/478389\/\" target=\"_blank\" rel=\"noopener\">Hyperellipsoidal clustering network (HEC)<\/a><\/li>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/991422\/\" target=\"_blank\" rel=\"noopener\">Self-splitting competitive learning network (SPLL)<\/a><\/li>\n<\/ul><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-2868\" class=\"elementor-tab-title\" data-tab=\"8\" role=\"button\" aria-controls=\"elementor-tab-content-2868\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-left\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><i class=\"fas fa-caret-right\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><i class=\"elementor-toggle-icon-opened fas fa-caret-up\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">VIII. Sequential or kernel algorithms<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-2868\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"8\" role=\"region\" aria-labelledby=\"elementor-tab-title-2868\"><ul>\n<li>Kernel-based\n<ul>\n<li><a href=\"http:\/\/citeseerx.ist.psu.edu\/viewdoc\/download?doi=10.1.1.135.4576&amp;rep=rep1&amp;type=pdf\" target=\"_blank\" rel=\"noopener\">Kernel k-means<\/a><\/li>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/1220297\/\" target=\"_blank\" rel=\"noopener\">Support vector clustering (SVC)<\/a><\/li>\n<\/ul>\n<\/li>\n<li>Sequential data\n<ul>\n<li><a href=\"https:\/\/www.semanticscholar.org\/paper\/Biological-Sequence-Analysis%3A-Probabilistic-Models-Durbin-Eddy\/571f5bbecd3a083a2bb6844f59a3f8cea237252e\" target=\"_blank\" rel=\"noopener\">Sequence Similarity<\/a><\/li>\n<li><a href=\"https:\/\/www.researchgate.net\/publication\/2903802_Pattern-Oriented_Hierarchical_Clustering\" target=\"_blank\" rel=\"noopener\">Indirect sequence clustering<\/a><\/li>\n<li><a href=\"https:\/\/proceedings.neurips.cc\/paper\/1996\/file\/6a61d423d02a1c56250dc23ae7ff12f3-Paper.pdf\" target=\"_blank\" rel=\"noopener\">Statistical HMM sequence clustering<\/a><\/li>\n<\/ul>\n<\/li>\n<\/ul><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-2869\" class=\"elementor-tab-title\" data-tab=\"9\" role=\"button\" aria-controls=\"elementor-tab-content-2869\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-left\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><i class=\"fas fa-caret-right\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><i class=\"elementor-toggle-icon-opened fas fa-caret-up\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">IX. Algorithms for Big Data (size or dimension)<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-2869\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"9\" role=\"region\" aria-labelledby=\"elementor-tab-title-2869\"><ul>\n<li>Large-scale data sets\n<ul>\n<li><a href=\"https:\/\/www.google.com\/url?sa=t&amp;rct=j&amp;q=&amp;esrc=s&amp;source=web&amp;cd=&amp;cad=rja&amp;uact=8&amp;ved=2ahUKEwiikKL65r3oAhUDExoKHfRyCRkQgAMoAHoECAEQAg&amp;url=http%3A%2F%2Fscholar.google.fr%2Fscholar_url%3Furl%3Dhttp%3A%2F%2Fbooks.google.fr%2Fbooks%253Fhl%253Dfr%2526lr%253D%2526id%253DYeFQHiikNo0C%2526oi%253Dfnd%2526pg%253DPR11%2526dq%253Dfinding%252BGroups%252Bin%252BData%3A%252BAn%252BIntroduction%252BTo%252BCluster%252BAnalysis%2526ots%253D5AvdC0PGtD%2526sig%253DHrW7OvnqC7gNp7dOaTlv70jsIL8%26hl%3Dfr%26sa%3DX%26scisig%3DAAGBfm3Ilhl5sM1Gy4ws9jYX8LV8U-r8iw%26nossl%3D1%26oi%3Dscholarr&amp;usg=AOvVaw1LTrDbTM5bBFklHaKspUj_\" target=\"_blank\" rel=\"noopener\">CLARA<\/a><\/li>\n<li><a href=\"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0306437901000084\" target=\"_blank\" rel=\"noopener\">PRIEST<\/a><\/li>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/1033770\/\" target=\"_blank\" rel=\"noopener\">CLARANS<\/a><\/li>\n<\/ul>\n<ul>\n<li><a href=\"https:\/\/www2.cs.sfu.ca\/CourseCentral\/459\/han\/papers\/zhang96.pdf\" target=\"_blank\" rel=\"noopener\">BIRCH<\/a><\/li>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/754966\/\" target=\"_blank\" rel=\"noopener\">BUBBLE<\/a><\/li>\n<li><a href=\"https:\/\/www.aaai.org\/Papers\/KDD\/1996\/KDD96-037.pdf\" target=\"_blank\" rel=\"noopener\">DBSCAN<\/a><\/li>\n<li><a href=\"https:\/\/pberba.github.io\/stats\/2020\/01\/17\/hdbscan\/\" target=\"_blank\" rel=\"noopener\">HDBSCAN<\/a><\/li>\n<li><a href=\"https:\/\/dl.acm.org\/action\/cookieAbsent\" target=\"_blank\" rel=\"noopener\">DENCLUDED<\/a><\/li>\n<li><a href=\"https:\/\/www.semanticscholar.org\/paper\/WaveCluster%3A-A-Multi-Resolution-Clustering-Approach-Sheikholeslami-Chatterjee\/f0015f0e834a84699a9b83c6c9af33acdac05069?p2df\" target=\"_blank\" rel=\"noopener\">WaveCluster<\/a><\/li>\n<li><a href=\"http:\/\/citeseerx.ist.psu.edu\/viewdoc\/download?doi=10.1.1.22.2543&amp;rep=rep1&amp;type=pdf\" target=\"_blank\" rel=\"noopener\">Fractal clustering<\/a><\/li>\n<li><a href=\"https:\/\/www.aaai.org\/Papers\/KDD\/1998\/KDD98-002.pdf\" target=\"_blank\" rel=\"noopener\">ART<\/a><\/li>\n<\/ul>\n<\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/dataviz\/\">dataviz<\/a> and high-dimensional data\n<ul>\n<li><a href=\"http:\/\/www.vision.jhu.edu\/teaching\/learning\/deeplearning19\/assets\/Baldi_Hornik-89.pdf\" target=\"_blank\" rel=\"noopener\">PCA with perceptron<\/a><\/li>\n<li><a href=\"http:\/\/users.ics.aalto.fi\/~juha\/papers\/Class_IEEETrNN-1997.pdf\" target=\"_blank\" rel=\"noopener\">ICA with perceptron<\/a><\/li>\n<li><a href=\"https:\/\/cds.cern.ch\/record\/683166\/files\/0471056693_TOC.pdf\" target=\"_blank\" rel=\"noopener\">Multidimensional scaling (MDS)<\/a><\/li>\n<li><a href=\"https:\/\/www.google.com\/url?sa=t&amp;rct=j&amp;q=&amp;esrc=s&amp;source=web&amp;cd=&amp;cad=rja&amp;uact=8&amp;ved=2ahUKEwiipYvr6L3oAhWszoUKHZrLDrcQgAMoAHoECAEQAg&amp;url=http%3A%2F%2Fscholar.google.fr%2Fscholar_url%3Furl%3Dhttp%3A%2F%2Fbooks.google.fr%2Fbooks%253Fhl%253Dfr%2526lr%253D%2526id%253DIMGzP-IIaKAC%2526oi%253Dfnd%2526pg%253DPR5%2526dq%253Dlearning%252BFrom%252BData%3A%252BConcepts%2C%252BTheory%2Cand%252BMethods%2526ots%253D2ixIYbidnW%2526sig%253D-eM7tBcRFn8vRYNcl_S84upIQas%26hl%3Dfr%26sa%3DX%26scisig%3DAAGBfm2TIx4NihkCBiRr1rXwYXEudFOkMA%26nossl%3D1%26oi%3Dscholarr&amp;usg=AOvVaw2UfNoZfjYSlWeaQZLY0Wwh\" target=\"_blank\" rel=\"noopener\">Projection pursuit<\/a><\/li>\n<li><a href=\"https:\/\/web.mit.edu\/cocosci\/Papers\/sci_reprint.pdf\" target=\"_blank\" rel=\"noopener\">Isomap<\/a><\/li>\n<li><a href=\"https:\/\/www.robots.ox.ac.uk\/~az\/lectures\/ml\/lle.pdf\" target=\"_blank\" rel=\"noopener\">Locally linear embedding (LLE)<\/a><\/li>\n<li><a href=\"https:\/\/www.cs.cornell.edu\/johannes\/papers\/1998\/sigmod1998-clique.pdf\" target=\"_blank\" rel=\"noopener\">Clustering in quest (CLICK)<\/a><\/li>\n<li><a href=\"https:\/\/www.semanticscholar.org\/paper\/Optimal-Grid-Clustering%3A-Towards-Breaking-the-Curse-Hinneburg-Keim\/6d0590cad1b154e2b905159855170b7bf453d8ac?p2df\" target=\"_blank\" rel=\"noopener\">OptiGrid<\/a><\/li>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/991713\/\" target=\"_blank\" rel=\"noopener\">Arbitrarily ORiented projected CLUster generation (ORCLUS)<\/a><\/li>\n<\/ul>\n<\/li>\n<\/ul><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-28610\" class=\"elementor-tab-title\" data-tab=\"10\" role=\"button\" aria-controls=\"elementor-tab-content-28610\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-left\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><i class=\"fas fa-caret-right\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><i class=\"elementor-toggle-icon-opened fas fa-caret-up\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">X. Tutorials<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-28610\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"10\" role=\"region\" aria-labelledby=\"elementor-tab-title-28610\"><p>Choice of methods:<\/p>\n<ul>\n<li><a href=\"https:\/\/towardsdatascience.com\/when-clustering-doesnt-make-sense-c6ed9a89e9e6\" target=\"_blank\" rel=\"noopener\">Cases where clustering is useless<\/a><\/li>\n<li><a href=\"https:\/\/towardsdatascience.com\/unsupervised-machine-learning-clustering-analysis-d40f2b34ae7e\" target=\"_blank\" rel=\"noopener\">Clustering, the basic algorithms to know<\/a><\/li>\n<li><a href=\"https:\/\/towardsdatascience.com\/17-clustering-algorithms-used-in-data-science-mining-49dbfa5bf69a\" target=\"_blank\" rel=\"noopener\">17 clustering algorithms for all use cases<\/a><\/li>\n<li><a href=\"https:\/\/towardsdatascience.com\/spectral-clustering-aba2640c0d5b\" target=\"_blank\" rel=\"noopener\">Introduction to Spectral Clustering<\/a><\/li>\n<\/ul>\n<p>Choice of metrics:<\/p>\n<ul>\n<li><a href=\"https:\/\/neptune.ai\/blog\/evaluation-metrics-binary-classification\" target=\"_blank\" rel=\"noopener\">24 metrics for binary classification<\/a><\/li>\n<\/ul>\n<p>Use case:<\/p>\n<ul>\n<li><a href=\"https:\/\/uxdesign.cc\/how-to-think-segmentation-from-day-1-f714df093ccb\" target=\"_blank\" rel=\"noopener\">Profiling \/ segmentation and use cases<\/a><\/li>\n<li><a href=\"https:\/\/towardsdatascience.com\/clustering-algorithms-for-customer-segmentation-af637c6830ac\" target=\"_blank\" rel=\"noopener\">Partitioning (clustering) and profiling<\/a><\/li>\n<\/ul><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-5b52b80 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"5b52b80\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-5e18a1a\" data-id=\"5e18a1a\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6ac44f40 elementor-widget elementor-widget-text-editor\" data-id=\"6ac44f40\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_89 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewbox=\"0 0 24 24\" version=\"1.2\" baseprofile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/#Partitionnement-de-donnees\" >Data partitioning<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Partitionnement-de-donnees\"><\/span>Data partitioning<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><\/p>\n<p class=\"wp-block-paragraph\">The process of data partitioning refers to the steps that represent the sequence required for a complete analysis. Implications of decisions taken in each of these areas:<\/p>\n<p><\/p>\n<ol class=\"wp-block-list\">\n<li>The entities to be grouped must be selected. The elements must be chosen to be representative of the structure of the clusters in the population.<\/li>\n<li>The variables to be used in the cluster analysis are selected. Again, the variables must contain enough information to allow grouping of objects.<\/li>\n<li>The user must decide whether or not to normalize the data. If normalization is to be performed, the user must select a procedure from among several different approaches.<\/li>\n<li>A measure of similarity or dissimilarity should be selected. These measurements reflect the degree of proximity or separation between objects.<\/li>\n<li>A clustering method must be selected. The user&#039;s concept of what constitutes a cluster is important because different methods have been devised to find different types of cluster structures.<\/li>\n<li>the <a href=\"https:\/\/complex-systems-ai.com\/en\/data-partitioning\/quality-over-number-of-clusters\/\">number of clusters<\/a> must be determined.<\/li>\n<li>The final step in the clustering process is to interpret, test, and replicate the cluster analysis. Interpreting clusters with the context applied requires the user&#039;s knowledge and expertise of the particular discipline. The tests involve the problem of determining whether there is a significant clustering or an arbitrary partition of the random noise. Finally, replication determines whether the resulting cluster structure can be replicated in other examples.<\/li>\n<\/ol>\n<p><\/p>\n<p class=\"wp-block-paragraph\">Although variations on this seven-phase process may be necessary to suit a particular application, this sequence represents the critical steps in a cluster analysis.<\/p>\n<p><\/p>\n<p class=\"wp-block-paragraph\">Data partitioning and classification are two fundamental tasks in data mining. Classification is primarily used as a supervised learning method, data partitioning for unsupervised learning (some partitioning models do both). The purpose of data partitioning is descriptive, that of classification is predictive. Since the purpose of data partitioning is to discover a new set of categories, the new groups are interesting in themselves and their evaluation is intrinsic. In classification tasks, however, an important part of the assessment is extrinsic, as the groups must reflect a set of reference classes.<\/p>\n<p><img fetchpriority=\"high\" decoding=\"async\" class=\"aligncenter wp-image-9873 size-full\" src=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2020\/09\/Cluster-Analysis-1000x468-1.jpg\" alt=\"data partitioning\" width=\"1000\" height=\"468\" title=\"\" srcset=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2020\/09\/Cluster-Analysis-1000x468-1.jpg 1000w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2020\/09\/Cluster-Analysis-1000x468-1-300x140.jpg 300w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2020\/09\/Cluster-Analysis-1000x468-1-768x359.jpg 768w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/><\/p>\n<p>Here is a 4V comparison of the most frequently used algorithms:<\/p>\n<p><img decoding=\"async\" class=\"alignnone wp-image-20963 size-large\" src=\"http:\/\/complex-systems-ai.com\/wp-content\/uploads\/2024\/02\/clustering1-1024x784.png\" alt=\"clustering\" width=\"1024\" height=\"784\" title=\"\" srcset=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2024\/02\/clustering1-1024x784.png 1024w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2024\/02\/clustering1-300x230.png 300w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2024\/02\/clustering1-768x588.png 768w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2024\/02\/clustering1-16x12.png 16w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2024\/02\/clustering1.png 1427w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/p>\n<p>The strengths and weaknesses of each category:<\/p>\n<p><img decoding=\"async\" class=\"alignnone wp-image-20966 size-large\" src=\"http:\/\/complex-systems-ai.com\/wp-content\/uploads\/2024\/02\/clustering2-1-1024x522.png\" alt=\"clustering\" width=\"1024\" height=\"522\" title=\"\" srcset=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2024\/02\/clustering2-1-1024x522.png 1024w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2024\/02\/clustering2-1-300x153.png 300w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2024\/02\/clustering2-1-768x392.png 768w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2024\/02\/clustering2-1-18x9.png 18w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2024\/02\/clustering2-1.png 1427w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/p>\n<p>As well as the most common comparison metrics:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-20967 size-large\" src=\"http:\/\/complex-systems-ai.com\/wp-content\/uploads\/2024\/02\/clustering3-1024x446.png\" alt=\"clustering\" width=\"1024\" height=\"446\" title=\"\" srcset=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2024\/02\/clustering3-1024x446.png 1024w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2024\/02\/clustering3-300x131.png 300w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2024\/02\/clustering3-768x335.png 768w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2024\/02\/clustering3-18x8.png 18w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2024\/02\/clustering3.png 1427w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/p>\n<p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"<p>Theories Home page Wiki I. Distance measure and Similarity function (data partitioning) Minkowski for numeric attributes For binary attributes For \u2026 <\/p>","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-8271","page","type-page","status-publish","hentry"],"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/pages\/8271","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/comments?post=8271"}],"version-history":[{"count":74,"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/pages\/8271\/revisions"}],"predecessor-version":[{"id":21150,"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/pages\/8271\/revisions\/21150"}],"wp:attachment":[{"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/media?parent=8271"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}