{"id":3685,"date":"2020-06-22T07:56:17","date_gmt":"2020-06-22T07:56:17","guid":{"rendered":"https:\/\/datascientest.com\/?p=3685"},"modified":"2023-11-09T14:13:52","modified_gmt":"2023-11-09T14:13:52","slug":"machine-learning-clustering-focus-sur-algorithme-cah","status":"publish","type":"post","link":"https:\/\/datascientest.com\/machine-learning-clustering-focus-sur-algorithme-cah","title":{"rendered":"Machine Learning &amp; Clustering : Focus sur l\u2019algorithme CAH"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"3685\" class=\"elementor elementor-3685\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-691b50c elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"691b50c\" data-element_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-9d34d9c\" data-id=\"9d34d9c\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-66eb2d0 elementor-widget elementor-widget-heading\" data-id=\"66eb2d0\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">Le clustering est une discipline particuli\u00e8re du Machine Learning ayant pour objectif de s\u00e9parer vos donn\u00e9es en groupes homog\u00e8nes ayant des caract\u00e9ristiques communes. C\u2019est un domaine tr\u00e8s appr\u00e9ci\u00e9 en marketing, par exemple, o\u00f9 l\u2019on cherche souvent \u00e0 segmenter les bases clients pour d\u00e9tecter des comportements particuliers.<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6e0cd52 elementor-widget elementor-widget-text-editor\" data-id=\"6e0cd52\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p><span style=\"font-weight: 400;\">Dans un<\/span> <a href=\"https:\/\/datascientest.com\/clustering-focus-sur-lalgorithme-des-k-moyenneshttps:\/\/datascientest.com\/clustering-focus-sur-lalgorithme-des-k-moyennes\" target=\"_blank\" rel=\"noopener\">pr\u00e9c\u00e9dent article<\/a><span style=\"font-weight: 400;\">, nous avions pr\u00e9sent\u00e9 un premier algorithme de clustering : celui des K-moyennes ou K-means.\u00a0<\/span><\/p><p>Dans cet article nous allons d\u00e9tailler le fonctionnement de l&rsquo;algorithme CAH.\u00a0La <strong>Classification Ascendante Hi\u00e9rarchique<\/strong> : <strong>CAH<\/strong> est un <strong>algorithme non supervis\u00e9<\/strong> tr\u00e8s connu en mati\u00e8re de <strong>Clustering<\/strong>.<\/p><blockquote><p><span style=\"font-weight: 400;\">Les champs d\u2019application sont divers\u00a0: segmentation client, analyse de donn\u00e9e, segmenter une image, apprentissage semi-supervis\u00e9\u2026.<\/span><\/p><\/blockquote>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a7e300d elementor-widget elementor-widget-heading\" data-id=\"a7e300d\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Le Principe <\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-227cb9d elementor-widget elementor-widget-text-editor\" data-id=\"227cb9d\" data-element_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<p><span style=\"font-weight: 400;\">\u00c9tant donn\u00e9s des points et un entier k, l&rsquo;algorithme vise \u00e0 diviser les points en k groupes, appel\u00e9s <\/span><b>clusters<\/b><span style=\"font-weight: 400;\">, homog\u00e8nes et compacts. Regardons l\u2019exemple ci-dessous\u00a0:<\/span><\/p>\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\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-d9c98c6 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d9c98c6\" data-element_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-50 elementor-top-column elementor-element elementor-element-af39e55\" data-id=\"af39e55\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b854384 elementor-widget elementor-widget-image\" data-id=\"b854384\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"383\" height=\"374\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/06\/unnamed-4.png\" class=\"attachment-large size-large wp-image-3696\" alt=\"clusters\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/06\/unnamed-4.png 383w, https:\/\/datascientest.com\/wp-content\/uploads\/2020\/06\/unnamed-4-300x293.png 300w\" sizes=\"(max-width: 383px) 100vw, 383px\" \/>\t\t\t\t\t\t\t\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-50 elementor-top-column elementor-element elementor-element-ab8a16a\" data-id=\"ab8a16a\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a6a5cf2 elementor-widget elementor-widget-image\" data-id=\"a6a5cf2\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"379\" height=\"383\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/06\/unnamed-5.png\" class=\"attachment-large size-large wp-image-3697\" alt=\"clusters\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/06\/unnamed-5.png 379w, https:\/\/datascientest.com\/wp-content\/uploads\/2020\/06\/unnamed-5-297x300.png 297w\" sizes=\"(max-width: 379px) 100vw, 379px\" \/>\t\t\t\t\t\t\t\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\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-c8afbd5 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c8afbd5\" data-element_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-861af2d\" data-id=\"861af2d\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a340dc6 elementor-widget elementor-widget-text-editor\" data-id=\"a340dc6\" data-element_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<p><span style=\"font-weight: 400;\">Sur ce <\/span><b>jeu de donn\u00e9es en 2D<\/b><span style=\"font-weight: 400;\"> il appara\u00eet clair que l\u2019<\/span><b>on peut le diviser en 3 groupes<\/b><span style=\"font-weight: 400;\">. Concr\u00e8tement comment s\u2019y prend-on\u00a0?\u00a0<\/span><\/p><ul><li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">L\u2019id\u00e9e de d\u00e9part est de consid\u00e9rer que <\/span><b>chacun des points de votre jeu de donn\u00e9es est un centro\u00efde<\/b><span style=\"font-weight: 400;\">. Cela revient \u00e0 consid\u00e9rer qu<\/span><b>\u2019\u00e0 chaque point correspond une unique \u00e9tiquette <\/b><span style=\"font-weight: 400;\">(0,1,2,3, 4\u2026).\u00a0<\/span><\/li><li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Ensuite <\/span><b>on regroupe chaque centro\u00efde avec son centro\u00efde voisin le plus proche<\/b><span style=\"font-weight: 400;\">. Ce dernier prend l\u2019\u00e9tiquette du centro\u00efde qui l\u2019a \u00ab\u00a0absorb\u00e9\u00a0\u00bb.\u00a0<\/span><\/li><li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">On <\/span><b>calcule<\/b><span style=\"font-weight: 400;\"> alors les <\/span><b>nouveaux centro\u00efdes<\/b><span style=\"font-weight: 400;\"> qui seront les <\/span><b>centres de gravit\u00e9 des clusters nouvellement cr\u00e9\u00e9s<\/b><span style=\"font-weight: 400;\">.\u00a0<\/span><\/li><li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">On <\/span><b>r\u00e9it\u00e8re l\u2019op\u00e9ration <\/b><span style=\"font-weight: 400;\">jusqu\u2019\u00e0 <\/span><b>obtenir un unique cluster<\/b><span style=\"font-weight: 400;\"> ou bien un nombre de clusters pr\u00e9alablement d\u00e9fini.\u00a0<\/span><\/li><\/ul><p><span style=\"font-weight: 400;\">Dans notre exemple le nombre de clusters optimal est 3 et le r\u00e9sultat final se trouve sur la figure de droite.<\/span><\/p>\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\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-c5ee648 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c5ee648\" data-element_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-eaa35b9\" data-id=\"eaa35b9\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6eb5044 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"6eb5044\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-md\" href=\"https:\/\/datascientest.com\/formations-data\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">D\u00e9couvrir nos formations en Data Science <\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\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\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-b3fe8a9 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"b3fe8a9\" data-element_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-b2056b6\" data-id=\"b2056b6\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f4877b8 elementor-widget elementor-widget-text-editor\" data-id=\"f4877b8\" data-element_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<p><span style=\"font-weight: 400;\">Pour mieux comprendre le m\u00e9canisme on peut le repr\u00e9senter avec un sch\u00e9ma simple\u00a0:<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8103564 elementor-widget elementor-widget-image\" data-id=\"8103564\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"404\" height=\"336\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/06\/Capture-d\u2019e\u0301cran-2020-06-12-a\u0300-16.12.42.png\" class=\"attachment-large size-large wp-image-3702\" alt=\"jeu de donn\u00e9es en 2D\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/06\/Capture-d\u2019e\u0301cran-2020-06-12-a\u0300-16.12.42.png 404w, https:\/\/datascientest.com\/wp-content\/uploads\/2020\/06\/Capture-d\u2019e\u0301cran-2020-06-12-a\u0300-16.12.42-300x250.png 300w\" sizes=\"(max-width: 404px) 100vw, 404px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6d7fb03 elementor-widget elementor-widget-text-editor\" data-id=\"6d7fb03\" data-element_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<p><span style=\"font-weight: 400;\">Au d\u00e9but, chaque lettre est consid\u00e9r\u00e9e comme un cluster puis on les regroupe au fur et \u00e0 mesure selon leur proximit\u00e9 pour ne former \u00e0 la fin qu\u2019un seul groupe\u00a0: abcdef.<\/span><\/p>\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\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-fa16ee2 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"fa16ee2\" data-element_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-7fe6196\" data-id=\"7fe6196\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-727ee82 elementor-widget elementor-widget-heading\" data-id=\"727ee82\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Notion de distance et crit\u00e8re <\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3f5992a elementor-widget elementor-widget-text-editor\" data-id=\"3f5992a\" data-element_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<p><span style=\"font-weight: 400;\">Vous l\u2019aurez compris dans cet algorithme trois points sont\u00a0 cl\u00e9\u00a0:<\/span><\/p><blockquote><p><span style=\"font-weight: 400;\">Quelle est la m\u00e9trique utilis\u00e9e pour \u00e9valuer la distance entre les centro\u00efdes ? Quel est le nombre de clusters \u00e0 choisir\u00a0? Sur quel crit\u00e8re d\u00e9cide-t-on de regrouper les centro\u00efdes entre eux\u00a0?<\/span><\/p><\/blockquote><p><span style=\"font-weight: 400;\">Dans la classification ascendante hi\u00e9rarchique g\u00e9n\u00e9ralement on utilise la <\/span><b>distance euclidienne<\/b><span style=\"font-weight: 400;\">, soient p = (p1,\u2026.,pn) et q = (q1,\u2026.,qn)\u00a0<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-65233c8 elementor-widget elementor-widget-image\" data-id=\"65233c8\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"79\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/06\/distance-euclidienne.png\" class=\"attachment-large size-large wp-image-3703\" alt=\"distance euclidienne\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/06\/distance-euclidienne.png 833w, https:\/\/datascientest.com\/wp-content\/uploads\/2020\/06\/distance-euclidienne-300x30.png 300w, https:\/\/datascientest.com\/wp-content\/uploads\/2020\/06\/distance-euclidienne-768x76.png 768w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\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\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-bdb3a01 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"bdb3a01\" data-element_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-0f4befb\" data-id=\"0f4befb\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7bc791d elementor-widget elementor-widget-text-editor\" data-id=\"7bc791d\" data-element_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<p><span style=\"font-weight: 400;\">Elle permet d\u2019<\/span><b>\u00e9valuer la distance entre les centro\u00efdes<\/b><span style=\"font-weight: 400;\">. A chaque \u00e9tape de regroupement entre deux centro\u00efdes on obtiendra un nouveau cluster et un<\/span><b> nouveau centro\u00efde qui n\u2019est autre que le centre de gravit\u00e9 du nuage de points<\/b><span style=\"font-weight: 400;\"> comme expliqu\u00e9 plus haut.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">C\u2019est l\u00e0 qu\u2019intervient la notion d\u2019<\/span><b>inertie intraclasse<\/b><span style=\"font-weight: 400;\">. Il s\u2019agit simplement de la <\/span><b>somme des distances euclidiennes entre chaque point associ\u00e9 au cluster et le centre gravit\u00e9 nouvellement calcul\u00e9<\/b><span style=\"font-weight: 400;\">. Bien \u00e9videmment quand vous allez <\/span><b>regrouper des clusters<\/b><span style=\"font-weight: 400;\"> entre eux vous allez <\/span><b>augmenter l\u2019inertie intraclasse<\/b><span style=\"font-weight: 400;\">. Tout le <\/span><b>challenge<\/b><span style=\"font-weight: 400;\"> est de faire en sorte de <\/span><b>minimiser cette augmentation<\/b><span style=\"font-weight: 400;\">. Ainsi si vous avez le choix de regrouper un cluster avec 4 ou 5 autres clusters diff\u00e9rents vous choisirez celui qui minimise le gain d\u2019inertie intraclasse. Cette m\u00e9thode est le principe du <\/span><b>crit\u00e8re de Ward <\/b><span style=\"font-weight: 400;\">qui est tr\u00e8s souvent utilis\u00e9 pour la <\/span><b>Classification Ascendante Hi\u00e9rarchique<\/b><span style=\"font-weight: 400;\">. Il existe d\u2019autres crit\u00e8res mais le <\/span><b>crit\u00e8re de Ward<\/b><span style=\"font-weight: 400;\"> est celui utilis\u00e9 par d\u00e9faut quand on <\/span><b>impl\u00e9mente une CAH en python avec Scikit Learn<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">Dans l\u2019exemple pr\u00e9c\u00e9dent il \u00e9tait ais\u00e9 de <\/span><b>trouver le nombre id\u00e9al<\/b><span style=\"font-weight: 400;\"> de clusters simplement en <\/span><b>visualisant graphiquement<\/b><span style=\"font-weight: 400;\">. G\u00e9n\u00e9ralement les jeux de donn\u00e9es ont plus de deux dimensions et il est donc difficile de visualiser le nuage de points et d\u2019identifier rapidement le nombre de clusters optimal. On peut d\u00e9cider la CAH en lui donnant un nombre de clusters au pr\u00e9alable. Supposons dans l\u2019exemple pr\u00e9c\u00e9dent que nous n\u2019avions pas visualis\u00e9 les donn\u00e9es avant et nous d\u00e9cidons de tester diff\u00e9rentes fois avec un nombre de clusters diff\u00e9rents. Voici les r\u00e9sultats obtenus\u00a0:<\/span><\/p>\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\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-2b96bbf elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2b96bbf\" data-element_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-daa0fea\" data-id=\"daa0fea\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-0565bb4 elementor-widget elementor-widget-image\" data-id=\"0565bb4\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"512\" height=\"140\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/06\/unnamed-6.png\" class=\"attachment-large size-large wp-image-3698\" alt=\"clusters diff\u00e9rents\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/06\/unnamed-6.png 512w, https:\/\/datascientest.com\/wp-content\/uploads\/2020\/06\/unnamed-6-300x82.png 300w\" sizes=\"(max-width: 512px) 100vw, 512px\" \/>\t\t\t\t\t\t\t\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\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-597f537 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"597f537\" data-element_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-e1b72eb\" data-id=\"e1b72eb\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-95d5662 elementor-widget elementor-widget-text-editor\" data-id=\"95d5662\" data-element_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<p><span style=\"font-weight: 400;\">Le partitionnement est inexact car le nombre de clusters optimal est 3. Une m\u00e9thode connue pour trouver le nombre de cluster optimal est d\u2019<\/span><b>utiliser un dendrogramme <\/b><span style=\"font-weight: 400;\">comme celui-ci\u00a0:<\/span><\/p>\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\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-fe629f0 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"fe629f0\" data-element_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-0ad81da\" data-id=\"0ad81da\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-1a43391 elementor-widget elementor-widget-image\" data-id=\"1a43391\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"512\" height=\"413\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/06\/unnamed-7.png\" class=\"attachment-large size-large wp-image-3699\" alt=\"dendrogramme\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/06\/unnamed-7.png 512w, https:\/\/datascientest.com\/wp-content\/uploads\/2020\/06\/unnamed-7-300x242.png 300w\" sizes=\"(max-width: 512px) 100vw, 512px\" \/>\t\t\t\t\t\t\t\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\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-9388d00 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9388d00\" data-element_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-6141e45\" data-id=\"6141e45\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e366490 elementor-widget elementor-widget-text-editor\" data-id=\"e366490\" data-element_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<p><span style=\"font-weight: 400;\">Il permet de<\/span><b> visualiser les regroupements successifs<\/b><span style=\"font-weight: 400;\"> jusqu\u2019\u00e0 obtenir un unique cluster. Il est souvent pertinent de choisir le partitionnement correspondant au plus grand saut entre deux clusters cons\u00e9cutifs.\u00a0<\/span><\/p><p><b>Le nombre de clusters correspond alors au nombre de lignes verticales travers\u00e9e par la coupe horizontale du dendrogramme.<\/b><span style=\"font-weight: 400;\"> Dans notre exemple ci-dessus la coupe horizontale correspond aux deux lignes rouges. Il y a 3 lignes verticales travers\u00e9es par la coupe. On en d\u00e9duit que le nombre de clusters optimal est 3.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-21640bf elementor-widget elementor-widget-heading\" data-id=\"21640bf\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Passons \u00e0 la pratique en Python <\/h2>\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\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-fccc1c7 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"fccc1c7\" data-element_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-0bed62d\" data-id=\"0bed62d\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7f48275 elementor-widget elementor-widget-text-editor\" data-id=\"7f48275\" data-element_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<blockquote><p><span style=\"font-weight: 400;\">L\u2019entra\u00eenement d\u2019une Classification Ascendante Hi\u00e9rarchique est facilit\u00e9 avec la librairie\u00a0<\/span><a href=\"https:\/\/scikit-learn.org\/stable\/\" target=\"_blank\" rel=\"noopener\">Scikit-Learn.<\/a><span style=\"font-weight: 400;\">\u00a0Un jeu de donn\u00e9es comme celui qui a servi d\u2019exemple se cr\u00e9e facilement avec la fonctionalit\u00e9 make_blobs de Scikit-Learn.<\/span><\/p><\/blockquote><p>Une fois notre jeu de donn\u00e9es g\u00e9n\u00e9r\u00e9s, nous pouvons passer \u00e0 l\u2019<b>impl\u00e9mentation d\u2019une CAH<\/b>. Nous allons utiliser la librairie Scikit-Learn.\u00a0<\/p><p>Voici comment l\u2019impl\u00e9menter en code :<\/p>\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\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-1aabd3f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"1aabd3f\" data-element_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-8cf6410\" data-id=\"8cf6410\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c56ed3a elementor-widget elementor-widget-shortcode\" data-id=\"c56ed3a\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><script src=\"https:\/\/gist.github.com\/Thibault-venet\/3f7bd86a28508b8fed42aef1772d8ece.js\"><\/script><\/div>\n\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\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-8de5b45 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"8de5b45\" data-element_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-56fdda8\" data-id=\"56fdda8\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-ea42956 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"ea42956\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-md\" href=\"https:\/\/datascientest.com\/formations-data\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Apprendre \u00e0 programmer en Python<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\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\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-23c9a17 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"23c9a17\" data-element_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-390217e\" data-id=\"390217e\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b4990c9 elementor-widget elementor-widget-text-editor\" data-id=\"b4990c9\" data-element_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<p><span style=\"font-weight: 400;\">L\u2019attribut n_clusters permet de renseigner le nombre de clusters souhait\u00e9 pour la classification. Par d\u00e9faut la distance utilis\u00e9e est la distance euclidienne. \u00c9galement le <\/span><b>\u00ab\u00a0crit\u00e8re de regroupement\u00a0\u00bb<\/b><span style=\"font-weight: 400;\"> appel\u00e9 <\/span><b>linkage est par d\u00e9faut le crit\u00e8re de Ward<\/b><span style=\"font-weight: 400;\">.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-14281bc elementor-widget elementor-widget-heading\" data-id=\"14281bc\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Les limites de la Classification Ascendante Hi\u00e9rarchique <\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fbba1bc elementor-widget elementor-widget-text-editor\" data-id=\"fbba1bc\" data-element_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<p><span style=\"font-weight: 400;\">Comme nous l\u2019avons vu l\u2019algorithme demande en amont de <\/span><b>d\u00e9finir le nombre de partitions<\/b><span style=\"font-weight: 400;\">. Cela demande d\u2019<\/span><b>en avoir une id\u00e9e pr\u00e9cise<\/b><span style=\"font-weight: 400;\"> et m\u00eame si nous avons vu une m\u00e9thode pour optimiser sur certains jeux de donn\u00e9es elle n\u2019est pas infaillible. En effet les jeux de donn\u00e9es avec des formes elliptiques par exemple poseront probl\u00e8me. Dans l\u2019exemple ci-dessous on voit clairement deux groupes s\u2019apparentent \u00e0 deux ellipses. La figure de droite montre le r\u00e9sultat apr\u00e8s utilisation d\u2019une CAH. Il est clair que celle-ci n\u2019a pas r\u00e9ussi \u00e0 identifier nos deux ellipses.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a6fb9f9 elementor-widget elementor-widget-image\" data-id=\"a6fb9f9\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"512\" height=\"220\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/06\/CAH4.png\" class=\"attachment-large size-large wp-image-3692\" alt=\"CAH\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/06\/CAH4.png 512w, https:\/\/datascientest.com\/wp-content\/uploads\/2020\/06\/CAH4-300x129.png 300w\" sizes=\"(max-width: 512px) 100vw, 512px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8072216 elementor-widget elementor-widget-text-editor\" data-id=\"8072216\" data-element_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<p><span style=\"font-weight: 400;\">\u00c9galement la classification ascendante hi\u00e9rarchique peut s\u2019av\u00e9rer rapidement tr\u00e8s co\u00fbteuse en temps et en ressource. Un moyen de <\/span><b>contourner ce probl\u00e8me<\/b><span style=\"font-weight: 400;\"> est de renseigner une <\/span><b>matrice de connectivit\u00e9<\/b><span style=\"font-weight: 400;\">. Il s\u2019agit d\u2019une <\/span><b>matrice creuse<\/b><span style=\"font-weight: 400;\"> indiquant <\/span><b>quelles paires d\u2019observation sont voisines<\/b><span style=\"font-weight: 400;\">.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9034196 elementor-widget elementor-widget-text-editor\" data-id=\"9034196\" data-element_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<p><span style=\"font-weight: 400;\">L\u2019utilisation d\u2019un algorithme de clustering tel que la Classification Ascendante Hi\u00e9rarchique n\u2019est pas ais\u00e9e suivant les donn\u00e9es dont vous disposez. Il faudra souvent les retravailler. Chez <strong>Datascientest<\/strong> nous vous apprendrons \u00e0 ma\u00eetriser les principaux algorithmes de clustering ainsi qu\u2019\u00e0 les interpr\u00e9ter sur des jeux de donn\u00e9es. <\/span><\/p><p><span style=\"font-weight: 400;\">N\u2019h\u00e9sitez pas \u00e0 venir <a href=\"https:\/\/datascientest.com\/formations-data\">d\u00e9couvrir nos offres de formation<\/a><\/span><span style=\"font-weight: 400;\">\u00a0pour apprendre \u00e0 ma\u00eetriser les algorithme de clustering mais \u00e9galement de nombreux d\u2019autres domaines du Machine Learning.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8be631b elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"8be631b\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/formations-data\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">D\u00e9marrez une formation en Data Science<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\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\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Le clustering est une discipline particuli\u00e8re du Machine Learning ayant pour objectif de s\u00e9parer vos donn\u00e9es en groupes homog\u00e8nes ayant des caract\u00e9ristiques communes. C\u2019est un domaine tr\u00e8s appr\u00e9ci\u00e9 en marketing, par exemple, o\u00f9 l\u2019on cherche souvent \u00e0 segmenter les bases clients pour d\u00e9tecter des comportements particuliers. Dans un pr\u00e9c\u00e9dent article, nous avions pr\u00e9sent\u00e9 un premier [&hellip;]<\/p>\n","protected":false},"author":77,"featured_media":3700,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2497],"tags":[197,1249,162],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Machine Learning &amp; Clustering : Focus sur l\u2019algorithme CAH -<\/title>\n<meta name=\"description\" content=\"Tout savoir sur La Classification Ascendante hi\u00e9rarchique ou CAH, un algorithme non supervis\u00e9 tr\u00e8s connu en mati\u00e8re de Clustering.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/machine-learning-clustering-focus-sur-algorithme-cah\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Machine Learning &amp; Clustering : Focus sur l\u2019algorithme CAH\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datascientest.com\/machine-learning-clustering-focus-sur-algorithme-cah\" \/>\n<meta property=\"og:site_name\" content=\"Formation Data Science | DataScientest.com\" \/>\n<meta property=\"article:published_time\" content=\"2020-06-22T07:56:17+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-11-09T14:13:52+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/06\/unnamed.png\" \/>\n\t<meta property=\"og:image:width\" content=\"379\" \/>\n\t<meta property=\"og:image:height\" content=\"383\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Raphael Kassel\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u00c9crit par\" \/>\n\t<meta name=\"twitter:data1\" content=\"Raphael Kassel\" \/>\n\t<meta name=\"twitter:label2\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data2\" content=\"7 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/datascientest.com\/machine-learning-clustering-focus-sur-algorithme-cah\",\"url\":\"https:\/\/datascientest.com\/machine-learning-clustering-focus-sur-algorithme-cah\",\"name\":\"Machine Learning &amp; Clustering : Focus sur l\u2019algorithme CAH -\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/#website\"},\"datePublished\":\"2020-06-22T07:56:17+00:00\",\"dateModified\":\"2023-11-09T14:13:52+00:00\",\"author\":{\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/6eca16f63daaafcbc96e15549b9d4ee1\"},\"description\":\"Tout savoir sur La Classification Ascendante hi\u00e9rarchique ou CAH, un algorithme non supervis\u00e9 tr\u00e8s connu en mati\u00e8re de Clustering.\",\"inLanguage\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/datascientest.com\/machine-learning-clustering-focus-sur-algorithme-cah\"]}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/datascientest.com\/#website\",\"url\":\"https:\/\/datascientest.com\/\",\"name\":\"Formation Data Science | DataScientest.com\",\"description\":\"Leader de la formation en Data Science\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/datascientest.com\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"fr-FR\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/6eca16f63daaafcbc96e15549b9d4ee1\",\"name\":\"Raphael Kassel\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/11\/raph.jpeg\",\"contentUrl\":\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/11\/raph.jpeg\",\"caption\":\"Raphael Kassel\"},\"description\":\"Raphael Kassel est le Head of Data Programs chez DataScientest. 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