{"id":10254,"date":"2023-05-20T11:19:38","date_gmt":"2023-05-20T05:49:38","guid":{"rendered":"https:\/\/neonpolice.com\/?p=10254"},"modified":"2024-03-27T13:19:11","modified_gmt":"2024-03-27T07:49:11","slug":"cluster-analysis-in-python","status":"publish","type":"post","link":"https:\/\/neonpolice.com\/fr\/cluster-analysis-in-python\/","title":{"rendered":"Un guide complet de l&#039;analyse de cluster en Python sur Data Camp"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">L&#039;analyse de cluster en Python est une technique fondamentale d&#039;exploration de donn\u00e9es et d&#039;apprentissage automatique utilis\u00e9e pour identifier des groupes ou des clusters au sein d&#039;un ensemble de donn\u00e9es. Il est largement appliqu\u00e9 dans divers domaines, notamment le marketing, la biologie, le traitement d\u2019images et la segmentation client. Python, avec son riche \u00e9cosyst\u00e8me de biblioth\u00e8ques, fournit des outils puissants pour effectuer une analyse de cluster de mani\u00e8re efficace et efficiente. Pour commencer, nous aborderons les concepts essentiels de l\u2019analyse typologique. Le clustering vise \u00e0 regrouper des objets similaires en fonction de leurs caract\u00e9ristiques et relations intrins\u00e8ques. Ces clusters sont form\u00e9s de telle mani\u00e8re que les objets d\u2019un m\u00eame cluster sont plus similaires les uns aux autres qu\u2019\u00e0 ceux des autres clusters. Le choix de l&#039;algorithme de clustering et des m\u00e9triques d&#039;\u00e9valuation d\u00e9pend de la nature des donn\u00e9es et du probl\u00e8me sp\u00e9cifique \u00e0 r\u00e9soudre.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Python propose plusieurs biblioth\u00e8ques puissantes pour l&#039;analyse de cluster, notamment scikit-learn, scipy et K-means. Scikit-learn fournit un ensemble complet d&#039;outils pour l&#039;apprentissage automatique, notamment divers algorithmes de clustering tels que K-means, DBSCAN et le clustering hi\u00e9rarchique. Scipy, une biblioth\u00e8que de calcul scientifique, propose des fonctions permettant d&#039;effectuer des regroupements hi\u00e9rarchiques et des calculs de distance. K-means est un algorithme populaire utilis\u00e9 pour partitionner les donn\u00e9es en un nombre pr\u00e9d\u00e9fini de clusters. Lisez l&#039;article suivant organis\u00e9 par un culte des tendances pour en savoir plus sur la meilleure analyse de cluster en Python, le meilleur cours de cluster Python et le cours de cluster Python en ligne.\u00a0<\/span><\/p>\n\t\t<div class=\"web-stories-list alignnone has-archive-link is-view-type-circles is-style-default is-carousel\" data-id=\"1\">\n\t\t\t<div\n\t\t\tclass=\"web-stories-list__inner-wrapper carousel-1\"\n\t\t\tstyle=\"--ws-circle-size:100px\"\n\t\t\t>\n\t\t\t\t\t\t\t\t\t<div\n\t\t\t\t\tclass=\"web-stories-list__carousel circles\"\n\t\t\t\t\tdata-id=\"carousel-1\"\n\t\t\t\t\tdata-prev=\"Pr\u00e9c.\"\n\t\t\t\t\tdata-next=\"Suivant\"\n\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<div\n\t\t\t\tclass=\"web-stories-list__story\"\n\t\t\t\tdata-wp-interactive=\"web-stories-block\"\n\t\t\t\tdata-wp-context='{\"instanceId\":1}'\t\t\t\tdata-wp-on--click=\"actions.open\"\n\t\t\t\tdata-wp-on-window--popstate=\"actions.onPopstate\"\n\t\t\t\t>\n\t\t\t\t\t\t\t<div class=\"web-stories-list__story-poster\">\n\t\t\t\t<a href=\"https:\/\/neonpolice.com\/fr\/web-stories\/your-ultimate-checklist-of-baby-essentials\/\" >\n\t\t\t\t\t<img\n\t\t\t\t\t\tsrc=\"https:\/\/neonpolice.com\/wp-content\/uploads\/2024\/01\/cropped-baby_essentials_checklist-hero-GettyImages-1413731369.webp\"\n\t\t\t\t\t\talt=\"Your Ultimate Checklist of Baby Essentials\"\n\t\t\t\t\t\twidth=\"185\"\n\t\t\t\t\t\theight=\"308\"\n\t\t\t\t\t\t\t\t\t\t\t\t\tsrcset=\"https:\/\/neonpolice.com\/wp-content\/uploads\/2024\/01\/cropped-baby_essentials_checklist-hero-GettyImages-1413731369.webp 640w, https:\/\/neonpolice.com\/wp-content\/uploads\/2024\/01\/cropped-baby_essentials_checklist-hero-GettyImages-1413731369-225x300.webp 225w, https:\/\/neonpolice.com\/wp-content\/uploads\/2024\/01\/cropped-baby_essentials_checklist-hero-GettyImages-1413731369-585x780.webp 585w, https:\/\/neonpolice.com\/wp-content\/uploads\/2024\/01\/cropped-baby_essentials_checklist-hero-GettyImages-1413731369-150x200.webp 150w\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsizes=\"auto, (max-width: 640px) 100vw, 640px\"\n\t\t\t\t\t\t\t\t\t\t\t\tloading=\"lazy\"\n\t\t\t\t\t\tdecoding=\"async\"\n\t\t\t\t\t>\n\t\t\t\t<\/a>\n\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<div\n\t\t\t\tclass=\"web-stories-list__story\"\n\t\t\t\tdata-wp-interactive=\"web-stories-block\"\n\t\t\t\tdata-wp-context='{\"instanceId\":1}'\t\t\t\tdata-wp-on--click=\"actions.open\"\n\t\t\t\tdata-wp-on-window--popstate=\"actions.onPopstate\"\n\t\t\t\t>\n\t\t\t\t\t\t\t<div class=\"web-stories-list__story-poster\">\n\t\t\t\t<a href=\"https:\/\/neonpolice.com\/fr\/web-stories\/youll-absolutely-love-these-moisturizers-for-dry-skin\/\" >\n\t\t\t\t\t<img\n\t\t\t\t\t\tsrc=\"https:\/\/neonpolice.com\/wp-content\/uploads\/2022\/10\/cropped-3-23.webp\"\n\t\t\t\t\t\talt=\"YOU\u2019LL ABSOLUTELY LOVE THESE MOISTURIZERS FOR DRY SKIN\"\n\t\t\t\t\t\twidth=\"185\"\n\t\t\t\t\t\theight=\"308\"\n\t\t\t\t\t\t\t\t\t\t\t\t\tsrcset=\"https:\/\/neonpolice.com\/wp-content\/uploads\/2022\/10\/cropped-3-23.webp 640w, https:\/\/neonpolice.com\/wp-content\/uploads\/2022\/10\/cropped-3-23-225x300.webp 225w, https:\/\/neonpolice.com\/wp-content\/uploads\/2022\/10\/cropped-3-23-585x780.webp 585w, https:\/\/neonpolice.com\/wp-content\/uploads\/2022\/10\/cropped-3-23-150x200.webp 150w\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsizes=\"auto, (max-width: 640px) 100vw, 640px\"\n\t\t\t\t\t\t\t\t\t\t\t\tloading=\"lazy\"\n\t\t\t\t\t\tdecoding=\"async\"\n\t\t\t\t\t>\n\t\t\t\t<\/a>\n\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<div\n\t\t\t\tclass=\"web-stories-list__story\"\n\t\t\t\tdata-wp-interactive=\"web-stories-block\"\n\t\t\t\tdata-wp-context='{\"instanceId\":1}'\t\t\t\tdata-wp-on--click=\"actions.open\"\n\t\t\t\tdata-wp-on-window--popstate=\"actions.onPopstate\"\n\t\t\t\t>\n\t\t\t\t\t\t\t<div class=\"web-stories-list__story-poster\">\n\t\t\t\t<a href=\"https:\/\/neonpolice.com\/fr\/web-stories\/top-18-white-sneakers-for-women\/\" >\n\t\t\t\t\t<img\n\t\t\t\t\t\tsrc=\"https:\/\/neonpolice.com\/wp-content\/uploads\/2022\/10\/cropped-10-2.webp\"\n\t\t\t\t\t\talt=\"WHITE SNEAKERS FOR WOMEN\"\n\t\t\t\t\t\twidth=\"185\"\n\t\t\t\t\t\theight=\"308\"\n\t\t\t\t\t\t\t\t\t\t\t\t\tsrcset=\"https:\/\/neonpolice.com\/wp-content\/uploads\/2022\/10\/cropped-10-2.webp 640w, https:\/\/neonpolice.com\/wp-content\/uploads\/2022\/10\/cropped-10-2-225x300.webp 225w, https:\/\/neonpolice.com\/wp-content\/uploads\/2022\/10\/cropped-10-2-585x780.webp 585w, https:\/\/neonpolice.com\/wp-content\/uploads\/2022\/10\/cropped-10-2-150x200.webp 150w\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsizes=\"auto, (max-width: 640px) 100vw, 640px\"\n\t\t\t\t\t\t\t\t\t\t\t\tloading=\"lazy\"\n\t\t\t\t\t\tdecoding=\"async\"\n\t\t\t\t\t>\n\t\t\t\t<\/a>\n\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<div\n\t\t\t\tclass=\"web-stories-list__story\"\n\t\t\t\tdata-wp-interactive=\"web-stories-block\"\n\t\t\t\tdata-wp-context='{\"instanceId\":1}'\t\t\t\tdata-wp-on--click=\"actions.open\"\n\t\t\t\tdata-wp-on-window--popstate=\"actions.onPopstate\"\n\t\t\t\t>\n\t\t\t\t\t\t\t<div class=\"web-stories-list__story-poster\">\n\t\t\t\t<a href=\"https:\/\/neonpolice.com\/fr\/web-stories\/what-clothing-brands-do-kids-like\/\" >\n\t\t\t\t\t<img\n\t\t\t\t\t\tsrc=\"https:\/\/neonpolice.com\/wp-content\/uploads\/2022\/10\/cropped-Untitled-design-8.webp\"\n\t\t\t\t\t\talt=\"WHAT CLOTHING BRANDS DO KIDS LIKE?\"\n\t\t\t\t\t\twidth=\"185\"\n\t\t\t\t\t\theight=\"308\"\n\t\t\t\t\t\t\t\t\t\t\t\t\tsrcset=\"https:\/\/neonpolice.com\/wp-content\/uploads\/2022\/10\/cropped-Untitled-design-8.webp 640w, https:\/\/neonpolice.com\/wp-content\/uploads\/2022\/10\/cropped-Untitled-design-8-225x300.webp 225w, https:\/\/neonpolice.com\/wp-content\/uploads\/2022\/10\/cropped-Untitled-design-8-585x780.webp 585w, https:\/\/neonpolice.com\/wp-content\/uploads\/2022\/10\/cropped-Untitled-design-8-150x200.webp 150w\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsizes=\"auto, (max-width: 640px) 100vw, 640px\"\n\t\t\t\t\t\t\t\t\t\t\t\tloading=\"lazy\"\n\t\t\t\t\t\tdecoding=\"async\"\n\t\t\t\t\t>\n\t\t\t\t<\/a>\n\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<div\n\t\t\t\tclass=\"web-stories-list__story\"\n\t\t\t\tdata-wp-interactive=\"web-stories-block\"\n\t\t\t\tdata-wp-context='{\"instanceId\":1}'\t\t\t\tdata-wp-on--click=\"actions.open\"\n\t\t\t\tdata-wp-on-window--popstate=\"actions.onPopstate\"\n\t\t\t\t>\n\t\t\t\t\t\t\t<div class=\"web-stories-list__story-poster\">\n\t\t\t\t<a href=\"https:\/\/neonpolice.com\/fr\/web-stories\/ways-to-reuse-your-wedding-dresses\/\" >\n\t\t\t\t\t<img\n\t\t\t\t\t\tsrc=\"https:\/\/neonpolice.com\/wp-content\/uploads\/2023\/10\/cropped-wedding-dress-640x853.jpg\"\n\t\t\t\t\t\talt=\"Ways To Reuse Your Wedding Dresses\"\n\t\t\t\t\t\twidth=\"185\"\n\t\t\t\t\t\theight=\"308\"\n\t\t\t\t\t\t\t\t\t\t\t\t\tsrcset=\"https:\/\/neonpolice.com\/wp-content\/uploads\/2023\/10\/cropped-wedding-dress.jpg 640w, https:\/\/neonpolice.com\/wp-content\/uploads\/2023\/10\/cropped-wedding-dress-225x300.jpg 225w, https:\/\/neonpolice.com\/wp-content\/uploads\/2023\/10\/cropped-wedding-dress-9x12.jpg 9w, https:\/\/neonpolice.com\/wp-content\/uploads\/2023\/10\/cropped-wedding-dress-585x780.jpg 585w, https:\/\/neonpolice.com\/wp-content\/uploads\/2023\/10\/cropped-wedding-dress-150x200.jpg 150w\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsizes=\"auto, (max-width: 640px) 100vw, 640px\"\n\t\t\t\t\t\t\t\t\t\t\t\tloading=\"lazy\"\n\t\t\t\t\t\tdecoding=\"async\"\n\t\t\t\t\t>\n\t\t\t\t<\/a>\n\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<div tabindex=\"0\" aria-label=\"Pr\u00e9c.\" class=\"glider-prev\"><\/div>\n\t\t\t\t\t<div tabindex=\"0\" aria-label=\"Suivant\" class=\"glider-next\"><\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_78 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\">Table des mati\u00e8res<\/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=\"Basculer la table des mati\u00e8res\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Basculer<\/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:\/\/neonpolice.com\/fr\/cluster-analysis-in-python\/#What_is_Cluster_Analysis\" >Qu\u2019est-ce que l\u2019analyse cluster ?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/neonpolice.com\/fr\/cluster-analysis-in-python\/#Preprocessing_Data_for_Cluster_Analysis\" >Pr\u00e9traitement des donn\u00e9es pour l&#039;analyse de cluster<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/neonpolice.com\/fr\/cluster-analysis-in-python\/#Popular_Clustering_Algorithms_and_Implementations_in_Python\" >Algorithmes de clustering et impl\u00e9mentations populaires en Python<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/neonpolice.com\/fr\/cluster-analysis-in-python\/#K-means_Clustering\" >Clustering K-means<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/neonpolice.com\/fr\/cluster-analysis-in-python\/#Hierarchical_Clustering\" >Classification hi\u00e9rarchique<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/neonpolice.com\/fr\/cluster-analysis-in-python\/#Density-Based_Clustering\" >Clustering bas\u00e9 sur la densit\u00e9<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/neonpolice.com\/fr\/cluster-analysis-in-python\/#Internal_Evaluation_Metrics\" >Param\u00e8tres d&#039;\u00e9valuation interne<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/neonpolice.com\/fr\/cluster-analysis-in-python\/#External_Evaluation_Metrics\" >Param\u00e8tres d&#039;\u00e9valuation externe<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/neonpolice.com\/fr\/cluster-analysis-in-python\/#Conclusion\" >Conclusion<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/neonpolice.com\/fr\/cluster-analysis-in-python\/#FAQs\" >FAQ<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"What_is_Cluster_Analysis\"><\/span><span style=\"font-weight: 400;\">Qu\u2019est-ce que l\u2019analyse cluster ?<\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<div id=\"attachment_10268\" style=\"width: 910px\" class=\"wp-caption alignnone\"><img decoding=\"async\" aria-describedby=\"caption-attachment-10268\" class=\"size-full wp-image-10268\" src=\"https:\/\/neonpolice.com\/wp-content\/uploads\/2023\/05\/what-is-cluster-analysis.webp\" alt=\"What is Cluster Analysis?\" width=\"900\" height=\"500\" srcset=\"https:\/\/neonpolice.com\/wp-content\/uploads\/2023\/05\/what-is-cluster-analysis.webp 900w, https:\/\/neonpolice.com\/wp-content\/uploads\/2023\/05\/what-is-cluster-analysis-300x167.webp 300w, https:\/\/neonpolice.com\/wp-content\/uploads\/2023\/05\/what-is-cluster-analysis-768x427.webp 768w, https:\/\/neonpolice.com\/wp-content\/uploads\/2023\/05\/what-is-cluster-analysis-18x10.webp 18w, https:\/\/neonpolice.com\/wp-content\/uploads\/2023\/05\/what-is-cluster-analysis-585x325.webp 585w, https:\/\/neonpolice.com\/wp-content\/uploads\/2023\/05\/what-is-cluster-analysis-150x83.webp 150w\" sizes=\"(max-width: 900px) 100vw, 900px\" \/><p id=\"caption-attachment-10268\" class=\"wp-caption-text\">Qu\u2019est-ce que l\u2019analyse cluster ? | N\u00e9onpolice<\/p><\/div>\n<p><a href=\"https:\/\/neonpolice.com\/fr\/acwb\/\" target=\"_blank\" rel=\"nofollow noopener sponsored\"><span style=\"font-weight: 400;\">Analyse de cluster en Python<\/span><\/a><span style=\"font-weight: 400;\"> est le processus de division d&#039;un ensemble de donn\u00e9es en groupes, ou clusters, en fonction de la similitude ou de la dissemblance des objets qu&#039;il contient. L&#039;objectif est de garantir que les objets d&#039;un m\u00eame cluster sont plus similaires les uns aux autres qu&#039;\u00e0 ceux des autres clusters. L&#039;analyse group\u00e9e a un large \u00e9ventail d&#039;applications, notamment la segmentation des clients, le traitement d&#039;images, l&#039;analyse de donn\u00e9es biologiques et la d\u00e9tection d&#039;anomalies. Pour comprendre l\u2019analyse typologique, il est important de se familiariser avec les concepts et la terminologie cl\u00e9s. Nous introduisons des termes tels que clusters, mesures de distance et centro\u00efdes. Les mesures de distance, telles que la distance euclidienne et la distance de Manhattan, mesurent la similarit\u00e9 entre les objets. Le centre de gravit\u00e9 repr\u00e9sente le point central d&#039;un cluster. De plus, nous discutons de la validit\u00e9 des clusters et des mesures d&#039;\u00e9valuation pour \u00e9valuer la qualit\u00e9 des r\u00e9sultats du clustering.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Preprocessing_Data_for_Cluster_Analysis\"><\/span><span style=\"font-weight: 400;\">Pr\u00e9traitement des donn\u00e9es pour l&#039;analyse de cluster<\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<div id=\"attachment_10266\" style=\"width: 910px\" class=\"wp-caption alignnone\"><img decoding=\"async\" aria-describedby=\"caption-attachment-10266\" class=\"size-full wp-image-10266\" src=\"https:\/\/neonpolice.com\/wp-content\/uploads\/2023\/05\/preprocessing-data-for-cluster-analysis.webp\" alt=\"Preprocessing Data for Cluster Analysis\" width=\"900\" height=\"500\" srcset=\"https:\/\/neonpolice.com\/wp-content\/uploads\/2023\/05\/preprocessing-data-for-cluster-analysis.webp 900w, https:\/\/neonpolice.com\/wp-content\/uploads\/2023\/05\/preprocessing-data-for-cluster-analysis-300x167.webp 300w, https:\/\/neonpolice.com\/wp-content\/uploads\/2023\/05\/preprocessing-data-for-cluster-analysis-768x427.webp 768w, https:\/\/neonpolice.com\/wp-content\/uploads\/2023\/05\/preprocessing-data-for-cluster-analysis-18x10.webp 18w, https:\/\/neonpolice.com\/wp-content\/uploads\/2023\/05\/preprocessing-data-for-cluster-analysis-585x325.webp 585w, https:\/\/neonpolice.com\/wp-content\/uploads\/2023\/05\/preprocessing-data-for-cluster-analysis-150x83.webp 150w\" sizes=\"(max-width: 900px) 100vw, 900px\" \/><p id=\"caption-attachment-10266\" class=\"wp-caption-text\">Pr\u00e9traitement des donn\u00e9es pour l&#039;analyse de cluster | N\u00e9onpolice<\/p><\/div>\n<p><a href=\"https:\/\/neonpolice.com\/fr\/acwb\/\" target=\"_blank\" rel=\"nofollow noopener sponsored\"><span style=\"font-weight: 400;\">Pr\u00e9traitement des donn\u00e9es<\/span><\/a><span style=\"font-weight: 400;\"> joue un r\u00f4le crucial dans l\u2019analyse de cluster en Python. Nous approfondissons les techniques de gestion des valeurs manquantes, des valeurs aberrantes et des variables cat\u00e9gorielles. Ces \u00e9tapes de pr\u00e9traitement garantissent que les donn\u00e9es sont dans un format appropri\u00e9 pour le clustering. \u200b\u200bLa s\u00e9lection des fonctionnalit\u00e9s est essentielle dans l&#039;analyse de cluster pour identifier les fonctionnalit\u00e9s les plus pertinentes pour le clustering. Nous explorons des techniques telles que l&#039;analyse en composantes principales (ACP) et le t-SNE pour la r\u00e9duction de dimensionnalit\u00e9, qui peuvent aider \u00e0 visualiser des donn\u00e9es de grande dimension et \u00e0 am\u00e9liorer les performances de clustering.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Popular_Clustering_Algorithms_and_Implementations_in_Python\"><\/span><span style=\"font-weight: 400;\">Algorithmes de clustering et impl\u00e9mentations populaires en Python<\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<div id=\"attachment_10270\" style=\"width: 910px\" class=\"wp-caption alignnone\"><img decoding=\"async\" aria-describedby=\"caption-attachment-10270\" class=\"size-full wp-image-10270\" src=\"https:\/\/neonpolice.com\/wp-content\/uploads\/2023\/05\/popular-clustering-algorithms-and-implementations-in-python.webp\" alt=\"Popular Clustering Algorithms and Implementations in Python\" width=\"900\" height=\"500\" srcset=\"https:\/\/neonpolice.com\/wp-content\/uploads\/2023\/05\/popular-clustering-algorithms-and-implementations-in-python.webp 900w, https:\/\/neonpolice.com\/wp-content\/uploads\/2023\/05\/popular-clustering-algorithms-and-implementations-in-python-300x167.webp 300w, https:\/\/neonpolice.com\/wp-content\/uploads\/2023\/05\/popular-clustering-algorithms-and-implementations-in-python-768x427.webp 768w, https:\/\/neonpolice.com\/wp-content\/uploads\/2023\/05\/popular-clustering-algorithms-and-implementations-in-python-18x10.webp 18w, https:\/\/neonpolice.com\/wp-content\/uploads\/2023\/05\/popular-clustering-algorithms-and-implementations-in-python-585x325.webp 585w, https:\/\/neonpolice.com\/wp-content\/uploads\/2023\/05\/popular-clustering-algorithms-and-implementations-in-python-150x83.webp 150w\" sizes=\"(max-width: 900px) 100vw, 900px\" \/><p id=\"caption-attachment-10270\" class=\"wp-caption-text\">Algorithmes de clustering et impl\u00e9mentations populaires en Python | N\u00e9onpolice<\/p><\/div>\n<h3><span class=\"ez-toc-section\" id=\"K-means_Clustering\"><\/span><span style=\"font-weight: 400;\">Clustering K-means<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><a href=\"https:\/\/neonpolice.com\/fr\/acwb\/\" target=\"_blank\" rel=\"nofollow noopener sponsored\"><span style=\"font-weight: 400;\">K-means clustering<\/span><\/a><span style=\"font-weight: 400;\"> est l\u2019un des algorithmes de clustering bas\u00e9s sur le partitionnement les plus utilis\u00e9s. Nous expliquons les principes derri\u00e8re K-means et d\u00e9montrons sa mise en \u0153uvre \u00e0 l&#039;aide de la biblioth\u00e8que scikit-learn. Nous discutons \u00e9galement des strat\u00e9gies permettant de s\u00e9lectionner le nombre optimal de clusters.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Hierarchical_Clustering\"><\/span><span style=\"font-weight: 400;\">Classification hi\u00e9rarchique<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><a href=\"https:\/\/neonpolice.com\/fr\/acwb\/\" target=\"_blank\" rel=\"nofollow noopener sponsored\"><span style=\"font-weight: 400;\">Classification hi\u00e9rarchique<\/span><\/a><span style=\"font-weight: 400;\"> est un algorithme puissant qui organise les donn\u00e9es dans une hi\u00e9rarchie de clusters. Nous expliquons les concepts de clustering hi\u00e9rarchique agglom\u00e9ratif et diviseur et pr\u00e9sentons leur impl\u00e9mentation avec la biblioth\u00e8que scipy. Les dendrogrammes sont pr\u00e9sent\u00e9s comme des repr\u00e9sentations visuelles des r\u00e9sultats du regroupement hi\u00e9rarchique.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Density-Based_Clustering\"><\/span><span style=\"font-weight: 400;\">Clustering bas\u00e9 sur la densit\u00e9<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><a href=\"https:\/\/neonpolice.com\/fr\/acwb\/\" target=\"_blank\" rel=\"nofollow noopener sponsored\"><span style=\"font-weight: 400;\">Clustering bas\u00e9 sur la densit\u00e9<\/span><\/a><span style=\"font-weight: 400;\"> les algorithmes, tels que DBSCAN, conviennent \u00e0 la d\u00e9couverte de groupes de formes arbitraires. Nous introduisons l&#039;algorithme DBSCAN et d\u00e9montrons sa mise en \u0153uvre \u00e0 l&#039;aide de scikit-learn. Nous discutons \u00e9galement de la mani\u00e8re d\u2019interpr\u00e9ter et d\u2019\u00e9valuer les r\u00e9sultats DBSCAN.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Internal_Evaluation_Metrics\"><\/span><span style=\"font-weight: 400;\">Param\u00e8tres d&#039;\u00e9valuation interne<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><a href=\"https:\/\/neonpolice.com\/fr\/acwb\/\" target=\"_blank\" rel=\"nofollow noopener sponsored\"><span style=\"font-weight: 400;\">\u00c9valuation<\/span><\/a><span style=\"font-weight: 400;\"> la qualit\u00e9 des r\u00e9sultats de clustering est cruciale pour \u00e9valuer l\u2019efficacit\u00e9 de l\u2019algorithme. Nous expliquons les mesures d&#039;\u00e9valuation internes telles que le coefficient Silhouette et l&#039;indice Davies-Bouldin, qui mesurent la coh\u00e9sion et la s\u00e9paration des clusters. Nous pr\u00e9sentons leur impl\u00e9mentation en Python.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"External_Evaluation_Metrics\"><\/span><span style=\"font-weight: 400;\">Param\u00e8tres d&#039;\u00e9valuation externe<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Dans certains cas, <\/span><a href=\"https:\/\/neonpolice.com\/fr\/acwb\/\" target=\"_blank\" rel=\"nofollow noopener sponsored\"><span style=\"font-weight: 400;\">param\u00e8tres d&#039;\u00e9valuation externe<\/span><\/a><span style=\"font-weight: 400;\"> sont utilis\u00e9s lorsque des \u00e9tiquettes de v\u00e9rit\u00e9 terrain sont disponibles. Nous introduisons des mesures telles que l&#039;indice Rand ajust\u00e9 (ARI) et l&#039;information mutuelle (MI), qui \u00e9valuent l&#039;accord entre les r\u00e9sultats du regroupement et la v\u00e9rit\u00e9 terrain. Nous d\u00e9montrons l&#039;utilisation de m\u00e9triques d&#039;\u00e9valuation externes en Python.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span><span style=\"font-weight: 400;\">Conclusion<\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Dans cet article, nous avons explor\u00e9 le monde de <a href=\"https:\/\/en.wikipedia.org\/wiki\/Cluster_analysis\" target=\"_blank\" rel=\"noopener\">l&#039;analyse par grappes<\/a> en Python et sa signification dans <\/span><a href=\"https:\/\/neonpolice.com\/fr\/acwb\/\" target=\"_blank\" rel=\"nofollow noopener sponsored\"><span style=\"font-weight: 400;\">d\u00e9couvrir des mod\u00e8les et des structures<\/span><\/a><span style=\"font-weight: 400;\"> au sein des ensembles de donn\u00e9es. Nous avons commenc\u00e9 par comprendre les concepts fondamentaux de l&#039;analyse cluster, notamment la d\u00e9finition des clusters, les mesures de distance et les centro\u00efdes. Nous avons ensuite approfondi les \u00e9tapes de pr\u00e9traitement n\u00e9cessaires \u00e0 la pr\u00e9paration des donn\u00e9es pour les regroupements, telles que la gestion des valeurs manquantes, des valeurs aberrantes et des variables cat\u00e9gorielles, ainsi que les techniques de s\u00e9lection de caract\u00e9ristiques et de r\u00e9duction de dimensionnalit\u00e9. Nous avons explor\u00e9 les algorithmes de clustering populaires disponibles en Python, notamment les K-means, le clustering hi\u00e9rarchique et le clustering bas\u00e9 sur la densit\u00e9. Gr\u00e2ce \u00e0 des exemples pratiques et \u00e0 des impl\u00e9mentations utilisant des biblioth\u00e8ques telles que scikit-learn et scipy, nous avons appris \u00e0 appliquer ces algorithmes \u00e0 nos ensembles de donn\u00e9es et \u00e0 interpr\u00e9ter les clusters r\u00e9sultants. Nous avons \u00e9galement discut\u00e9 de strat\u00e9gies pour d\u00e9terminer le nombre optimal de clusters et \u00e9valu\u00e9 la qualit\u00e9 des r\u00e9sultats de clustering \u00e0 l&#039;aide de mesures d&#039;\u00e9valuation internes et externes. C&#039;est tout ce que vous devez savoir sur l&#039;analyse de cluster en Python. De plus, visitez le site officiel du culte Trending pour en savoir plus sur l&#039;analyse de cluster en Python.\u00a0<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"FAQs\"><\/span><span style=\"font-weight: 400;\">FAQ<\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<div class=\"su-accordion su-u-trim\"><div class=\"su-spoiler su-spoiler-style-default su-spoiler-icon-plus su-spoiler-closed\" data-scroll-offset=\"0\" data-anchor-in-url=\"no\"><div class=\"su-spoiler-title\" tabindex=\"0\" role=\"button\"><span class=\"su-spoiler-icon\"><\/span>Comment faire une analyse de cluster avec Python ?<\/div><div class=\"su-spoiler-content su-u-clearfix su-u-trim\">\n<p>L&#039;analyse cluster peut \u00eatre effectu\u00e9e en Python \u00e0 l&#039;aide de diverses biblioth\u00e8ques et algorithmes. Voici un processus g\u00e9n\u00e9ral \u00e9tape par \u00e9tape pour effectuer une analyse de cluster\u00a0:<\/p>\n<p>Importez les biblioth\u00e8ques n\u00e9cessaires\u00a0: commencez par importer les biblioth\u00e8ques requises, telles que NumPy, pandas, scikit-learn et matplotlib.<\/p>\n<p>Chargez et pr\u00e9traitez les donn\u00e9es\u00a0: chargez votre ensemble de donn\u00e9es dans Python et pr\u00e9traitez-le si n\u00e9cessaire. Cela peut impliquer la gestion des valeurs manquantes, la mise \u00e0 l&#039;\u00e9chelle ou la normalisation des fonctionnalit\u00e9s et le codage des variables cat\u00e9gorielles.<br \/>\nChoisissez l&#039;algorithme de clustering appropri\u00e9\u00a0: il existe plusieurs algorithmes de clustering disponibles en Python, notamment les K-means, le clustering hi\u00e9rarchique et DBSCAN. S\u00e9lectionnez l&#039;algorithme en fonction des caract\u00e9ristiques et des exigences de vos donn\u00e9es.<br \/>\nCr\u00e9ez une instance de l&#039;algorithme de clustering\u00a0: instanciez l&#039;algorithme de clustering choisi avec les param\u00e8tres souhait\u00e9s.<br \/>\nAjuster l&#039;algorithme aux donn\u00e9es\u00a0: appliquez l&#039;algorithme de clustering \u00e0 l&#039;ensemble de donn\u00e9es pr\u00e9trait\u00e9 \u00e0 l&#039;aide de la m\u00e9thode fit(). Cette \u00e9tape calcule les clusters et attribue chaque point de donn\u00e9es \u00e0 un cluster.<br \/>\nAnalyser les r\u00e9sultats\u00a0: \u00e9valuez les r\u00e9sultats du clustering en analysant les clusters obtenus. Vous pouvez examiner les \u00e9tiquettes de cluster attribu\u00e9es \u00e0 chaque point de donn\u00e9es et explorer les caract\u00e9ristiques de chaque cluster.<br \/>\nVisualisez les clusters\u00a0: utilisez des techniques de visualisation de donn\u00e9es pour tracer les clusters et obtenir des informations. Cela peut impliquer la cr\u00e9ation de nuages de points, de cartes thermiques ou d&#039;autres m\u00e9thodes de visualisation.<\/p>\n<\/div><\/div> <div class=\"su-spoiler su-spoiler-style-default su-spoiler-icon-plus su-spoiler-closed\" data-scroll-offset=\"0\" data-anchor-in-url=\"no\"><div class=\"su-spoiler-title\" tabindex=\"0\" role=\"button\"><span class=\"su-spoiler-icon\"><\/span>A quoi sert l\u2019analyse cluster en Python ?<\/div><div class=\"su-spoiler-content su-u-clearfix su-u-trim\">\n<p>L&#039;analyse cluster est une technique puissante en Python qui a diverses applications dans diff\u00e9rents domaines. Certaines utilisations courantes de l&#039;analyse cluster en Python incluent\u00a0:<\/p>\n<p>Segmentation des clients\u00a0: l&#039;analyse group\u00e9e peut \u00eatre utilis\u00e9e pour regrouper les clients en fonction de leurs habitudes d&#039;achat, de leurs pr\u00e9f\u00e9rences ou de leurs donn\u00e9es d\u00e9mographiques. Cela aide les entreprises \u00e0 adapter leurs strat\u00e9gies marketing et \u00e0 am\u00e9liorer la satisfaction de leurs clients.<br \/>\nTraitement d&#039;image\u00a0: des algorithmes de clustering peuvent \u00eatre appliqu\u00e9s aux images pour des t\u00e2ches telles que la segmentation d&#039;images, la reconnaissance d&#039;objets et la compression d&#039;images.<br \/>\nD\u00e9tection des anomalies\u00a0: l&#039;analyse de cluster peut identifier les valeurs aberrantes ou les anomalies dans les ensembles de donn\u00e9es, aidant ainsi \u00e0 d\u00e9tecter les fraudes, les intrusions r\u00e9seau ou tout comportement anormal dans un syst\u00e8me.<br \/>\nRegroupement de documents\u00a0: l&#039;analyse de cluster peut \u00eatre utilis\u00e9e pour regrouper des documents similaires, facilitant ainsi des t\u00e2ches telles que la classification de textes, la mod\u00e9lisation de sujets et les syst\u00e8mes de recommandation.<br \/>\nG\u00e9nomique et bioinformatique\u00a0: l&#039;analyse group\u00e9e permet d&#039;identifier des mod\u00e8les dans les donn\u00e9es g\u00e9n\u00e9tiques, de classer les profils d&#039;expression g\u00e9nique et de d\u00e9couvrir les relations entre les g\u00e8nes.<\/p>\n<\/div><\/div> <div class=\"su-spoiler su-spoiler-style-default su-spoiler-icon-plus su-spoiler-closed\" data-scroll-offset=\"0\" data-anchor-in-url=\"no\"><div class=\"su-spoiler-title\" tabindex=\"0\" role=\"button\"><span class=\"su-spoiler-icon\"><\/span>Quel outil est utilis\u00e9 pour l\u2019analyse group\u00e9e\u00a0?<\/div><div class=\"su-spoiler-content su-u-clearfix su-u-trim\">\n<p>Python fournit plusieurs outils et biblioth\u00e8ques pour l&#039;analyse de cluster. Parmi les plus populaires, citons\u00a0:<\/p>\n<p>scikit-learn\u00a0: scikit-learn est une biblioth\u00e8que d&#039;apprentissage automatique largement utilis\u00e9e en Python qui propose divers algorithmes de clustering, notamment les K-means, le clustering hi\u00e9rarchique et DBSCAN.<br \/>\nscipy\u00a0: la biblioth\u00e8que scipy fournit des fonctions de calcul scientifique et inclut des algorithmes de clustering hi\u00e9rarchique et des m\u00e9triques de distance.<br \/>\npandas\u00a0: pandas est une puissante biblioth\u00e8que de manipulation de donn\u00e9es qui peut \u00eatre utilis\u00e9e pour pr\u00e9traiter et organiser les donn\u00e9es avant d&#039;appliquer des algorithmes de clustering.<br \/>\nMatplotlib et Seaborn\u00a0: ces biblioth\u00e8ques offrent une gamme de fonctionnalit\u00e9s de visualisation de donn\u00e9es, permettant la cr\u00e9ation de trac\u00e9s et de visualisations perspicaces de clusters.<\/p>\n<\/div><\/div>\n<div class=\"su-accordion su-u-trim\"><\/div><div class=\"su-spoiler su-spoiler-style-default su-spoiler-icon-plus su-spoiler-closed\" data-scroll-offset=\"0\" data-anchor-in-url=\"no\"><div class=\"su-spoiler-title\" tabindex=\"0\" role=\"button\"><span class=\"su-spoiler-icon\"><\/span>Comment tracer 3 clusters en Python ?<\/div><div class=\"su-spoiler-content su-u-clearfix su-u-trim\">\n<p>importer matplotlib.pyplot en tant que plt<\/p>\n<p>importer numpy en tant que np<\/p>\n<p># G\u00e9n\u00e9rer des donn\u00e9es al\u00e9atoires pour trois clusters<\/p>\n<p>al\u00e9atoire.seed(0)<\/p>\n<p>cluster1 = np.random.normal(2, 1, (50, 2))<\/p>\n<p>cluster2 = np.random.normal(5, 1, (50, 2))<\/p>\n<p>cluster3 = np.random.normal(8, 1, (50, 2))<\/p>\n<p># Concat\u00e9ner les clusters en un seul ensemble de donn\u00e9es<\/p>\n<p>donn\u00e9es = np.concatenate((cluster1, cluster2, cluster3))<\/p>\n<p># Tracer les clusters<\/p>\n<p>plt.scatter(donn\u00e9es[:, 0], donn\u00e9es[:, 1], s=50)<\/p>\n<p>plt.title(&#039;Trac\u00e9 de trois clusters&#039;)<\/p>\n<p>plt.xlabel(&#039;Axe X<\/p>\n<\/div><\/div><\/div>\n<p>&nbsp;<\/p>\n<p><script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [{\n    \"@type\": \"Question\",\n    \"name\": \"How to do cluster analysis with Python?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"Cluster analysis can be performed in Python using various libraries and algorithms. Here's a general step-by-step process to conduct cluster analysis:<\/p>\n<p>Import the necessary libraries: Begin by importing the required libraries, such as NumPy, pandas, scikit-learn, and matplotlib.<\/p>\n<p>Load and preprocess the data: Load your dataset into Python, and preprocess it as needed. This may involve handling missing values, scaling or normalizing features, and encoding categorical variables.\nChoose the appropriate clustering algorithm: There are several clustering algorithms available in Python, including K-means, hierarchical clustering, and DBSCAN. Select the algorithm based on your data characteristics and requirements.\nCreate an instance of the clustering algorithm: Instantiate the chosen clustering algorithm with the desired parameters.\nFit the algorithm to the data: Apply the clustering algorithm to the preprocessed dataset using the fit() method. This step calculates the clusters and assigns each data point to a cluster.\nAnalyze the results: Evaluate the clustering results by analyzing the obtained clusters. You can examine the cluster labels assigned to each data point and explore the characteristics of each cluster.\nVisualize the clusters: Use data visualization techniques to plot the clusters and gain insights. This may involve creating scatter plots, heatmaps, or other visualization methods.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"What is the use of cluster analysis in Python?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"Cluster analysis is a powerful technique in Python that has various applications across different domains. Some common uses of cluster analysis in Python include:<\/p>\n<p>Customer segmentation: Cluster analysis can be used to group customers based on their buying patterns, preferences, or demographics. This helps businesses tailor their marketing strategies and improve customer satisfaction.\nImage processing: Clustering algorithms can be applied to images for tasks such as image segmentation, object recognition, and image compression.\nAnomaly detection: Cluster analysis can identify outliers or anomalies in datasets, helping detect fraud, network intrusions, or any abnormal behaviour in a system.\nDocument clustering: Cluster analysis can be used to group similar documents together, aiding tasks such as text classification, topic modelling, and recommendation systems.\nGenomics and bioinformatics: Cluster analysis helps identify patterns in genetic data, classify gene expression profiles, and discover relationships between genes.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"Which tool is used for cluster analysis?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"Python provides several tools and libraries for cluster analysis. Some popular ones include:<\/p>\n<p>scikit-learn: scikit-learn is a widely-used machine learning library in Python that offers various clustering algorithms, including K-means, hierarchical clustering, and DBSCAN.\nscipy: The scipy library provides functions for scientific computing and includes hierarchical clustering algorithms and distance metrics.\npandas: pandas is a powerful data manipulation library that can be used for preprocessing and organizing data before applying clustering algorithms.\nMatplotlib and Seaborn: These libraries offer a range of data visualization capabilities, enabling the creation of insightful plots and visualizations of clusters.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"How to plot 3 clusters in Python?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"import matplotlib.pyplot as plt\nimport numpy as np<\/p>\n<p># Generate random data for three clusters\nnp. random.seed(0)\ncluster1 = np.random.normal(2, 1, (50, 2))\ncluster2 = np.random.normal(5, 1, (50, 2))\ncluster3 = np.random.normal(8, 1, (50, 2))<\/p>\n<p># Concatenate the clusters into a single dataset\ndata = np.concatenate((cluster1, cluster2, cluster3))<\/p>\n<p># Plot the clusters\nplt.scatter(data[:, 0], data[:, 1], s=50)\nplt.title('Plot of Three Clusters')\nplt.xlabel('X-axis\"\n    }\n  }]\n}\n<\/script><\/p>","protected":false},"excerpt":{"rendered":"<p>L&#039;analyse de cluster en Python est une technique fondamentale d&#039;exploration de donn\u00e9es et d&#039;apprentissage automatique utilis\u00e9e pour identifier des groupes ou des clusters au sein d&#039;un ensemble de donn\u00e9es. Il est largement appliqu\u00e9 dans divers domaines,\u2026<\/p>","protected":false},"author":2,"featured_media":10272,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[252,1016],"tags":[668],"class_list":["post-10254","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-technology","category-datacamp","tag-cluster-analysis-in-python"],"_links":{"self":[{"href":"https:\/\/neonpolice.com\/fr\/wp-json\/wp\/v2\/posts\/10254","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/neonpolice.com\/fr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/neonpolice.com\/fr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/neonpolice.com\/fr\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/neonpolice.com\/fr\/wp-json\/wp\/v2\/comments?post=10254"}],"version-history":[{"count":0,"href":"https:\/\/neonpolice.com\/fr\/wp-json\/wp\/v2\/posts\/10254\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/neonpolice.com\/fr\/wp-json\/wp\/v2\/media\/10272"}],"wp:attachment":[{"href":"https:\/\/neonpolice.com\/fr\/wp-json\/wp\/v2\/media?parent=10254"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/neonpolice.com\/fr\/wp-json\/wp\/v2\/categories?post=10254"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/neonpolice.com\/fr\/wp-json\/wp\/v2\/tags?post=10254"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}