{"id":172024,"date":"2023-10-15T16:21:23","date_gmt":"2023-10-15T15:21:23","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=172024"},"modified":"2023-11-10T11:32:49","modified_gmt":"2023-11-10T10:32:49","slug":"spectral-clustering-definition-operation-use","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/spectral-clustering-definition-operation-use","title":{"rendered":"Spectral Clustering: definition, operation, use"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"172024\" class=\"elementor elementor-172024\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7c8fe273 e-flex e-con-boxed e-con\" data-id=\"7c8fe273\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5836d944 elementor-widget elementor-widget-text-editor\" data-id=\"5836d944\" 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<h2>&#8220;Data drives all we do&#8221;, le slogan de Cambridge Analytica se v\u00e9rifie en permanence: les donn\u00e9es influencent et structurent nos choix, en tant que consommateurs, citoyens, politiques, entrepreneurs\u2026<\/h2>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6812f985 elementor-widget elementor-widget-text-editor\" data-id=\"6812f985\" 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>This data provides a wealth of decision-making information. On a small scale, it&#8217;s relatively simple. But<a href=\"https:\/\/datascientest.com\/en\/distinguishing-data-professions-unraveling-the-differences\"> data experts<\/a> need powerful tools to process and sort all this information on a macro scale (like the French population). Spectral clustering is just such a tool.<\/p><p>So, what exactly is spectral clustering, and how useful is it? What are the advantages and limitations of this model?<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-1ceb4fd8 e-flex e-con-boxed e-con\" data-id=\"1ceb4fd8\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-59f92f4e elementor-widget elementor-widget-heading\" data-id=\"59f92f4e\" 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><h3 class=\"elementor-heading-title elementor-size-default\">What is spectral clustering?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5d74cd2e elementor-widget elementor-widget-heading\" data-id=\"5d74cd2e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Definition<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2ee2fe49 elementor-widget elementor-widget-text-editor\" data-id=\"2ee2fe49\" 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>Spectral clustering is one of the <a href=\"https:\/\/datascientest.com\/en\/mlops-devops-applied-to-machine-learning-projects\">components of machine learning<\/a> and <a href=\"https:\/\/datascientest.com\/en\/artificial-intelligence-definition\">artificial intelligence.<\/a><\/p><p>It is a data partitioning algorithm based on spectral graph theory and linear algebra.<\/p><p>The idea is to segment a graph into several small groups with similar or nearby values.<\/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-760e2b elementor-widget elementor-widget-heading\" data-id=\"760e2b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">The difference between clustering and classification<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-23da82e0 elementor-widget elementor-widget-text-editor\" data-id=\"23da82e0\" 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>Classification involves grouping data according to a pre-defined class or group.<\/p><p>With clustering, on the other hand, the <strong>algorithm<\/strong> does not know the classes until it has grouped the data. This is referred to as an unsupervised learning technique.<\/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-1e19998b elementor-widget elementor-widget-heading\" data-id=\"1e19998b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">The different types of data partitioning<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-62d04a0b elementor-widget elementor-widget-text-editor\" data-id=\"62d04a0b\" 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>There are several types of data partitioning, which differ in terms of their operation and purpose. Spectral clustering is one of them:<\/p><ul><li><strong>K-means:<\/strong> the aim is to establish a reference average across a data set. The idea is to define a typical profile that can reach the greatest number of people. Spectral data partitioning, on the other hand, aims to create different groups with as many commonalities as possible.<\/li><li><strong>DBSCAN:<\/strong> this algorithm uses cluster distance and density to create sub-groups. Spectral clustering, on the other hand, is based on data similarity.<\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-1b4a0467 e-flex e-con-boxed e-con\" data-id=\"1b4a0467\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-60d127d9 elementor-widget elementor-widget-heading\" data-id=\"60d127d9\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Why use spectral clustering?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-234e3435 elementor-widget elementor-widget-heading\" data-id=\"234e3435\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Who uses spectral clustering?<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1d31d3cd elementor-widget elementor-widget-text-editor\" data-id=\"1d31d3cd\" 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>Spectral clustering is particularly used by marketing professionals to understand user or target behavior.<\/p><p><strong>But beware<\/strong>: this <a href=\"https:\/\/datascientest.com\/en\/algorithm-what-is-it\">algorithm<\/a> requires specific skills. As such, data analysts, data scientists and other data analysis experts are the ones who should be using it.<\/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-12210c51 elementor-widget elementor-widget-heading\" data-id=\"12210c51\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">In what areas is spectral clustering used?<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7a26238c elementor-widget elementor-widget-text-editor\" data-id=\"7a26238c\" 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>As spectral partitioning enables<a href=\"https:\/\/datascientest.com\/en\/apache-kafka-the-real-time-data-processing-platform\"> large-scale data processing,<\/a> this method is often used for marketing purposes. Companies use this algorithm to segment their targets according to their expectations, needs, profile, maturity level and so on.<\/p><p>Spectral clustering can also be used in politics, particularly during election periods. By separating the mass of voters into smaller groups, candidates can communicate with each of them in a more personalized way.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-7aad913d e-flex e-con-boxed e-con\" data-id=\"7aad913d\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-41907927 elementor-widget elementor-widget-heading\" data-id=\"41907927\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">How does the spectral clustering algorithm work?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1d04d7ff elementor-widget elementor-widget-text-editor\" data-id=\"1d04d7ff\" 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>The spectral clustering algorithm consists of several steps:<\/p><ul><li>Construction of a graph through the affinity matrix (or similarity matrix, adjacent matrix);<\/li><li>Segmentation of data points into smaller dimensional spaces;<\/li><li>use of eigenvalues and eigenvectors to define sub-graphs.<\/li><\/ul><p>To help you better understand how it works, we explain the three essential concepts of spectral clustering.<\/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-371aef09 elementor-widget elementor-widget-heading\" data-id=\"371aef09\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Graphics<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-121bdcde elementor-widget elementor-widget-image\" data-id=\"121bdcde\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"240\" height=\"144\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/10\/undirected-graph.png\" class=\"attachment-large size-large wp-image-172029\" alt=\"\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Non-oriented graph <\/br> Source : techiedelight.com<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\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-2cde2e07 elementor-widget elementor-widget-text-editor\" data-id=\"2cde2e07\" 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>Graphs are a set of nodes linked by a set of edges. These nodes and edges may or may not be linked.<\/p><p>But in any case, graphs can be used to represent many types of data.<\/p><p>For spectral clustering, the starting point is a graph, either neighborhood, nearest neighbor (KNN) or fully connected.<\/p><p>This is then translated into matrix form. More precisely, the adjacent matrix.<\/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-334fc0cf elementor-widget elementor-widget-heading\" data-id=\"334fc0cf\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Matrixes<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5d8221ba elementor-widget elementor-widget-text-editor\" data-id=\"5d8221ba\" 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>The adjacent matrix (A) summarizes the relationships between all the data points in the graph.<\/p><p>For example:<\/p><p>Interacting data = 1<br \/>Data that do not interact = 0<\/p><p>The matrix can then look like the table below:<\/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-53e392e3 elementor-widget elementor-widget-html\" data-id=\"53e392e3\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<table class=\"tftable\" border=\"1\">\r\n<tr><td>1<\/td><td>0<\/td><td>0<\/td><td>1<\/td><\/tr>\r\n<tr><td>0<\/td><td>1<\/td><td>1<\/td><td>1<\/td><\/tr>\r\n<tr><td>0<\/td><td>1<\/td><td>0<\/td><td>0<\/td><\/tr>\r\n<tr><td>0<\/td><td>1<\/td><td>0<\/td><td>0<\/td><\/tr>\r\n<\/table>\r\n\r\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3fe25988 elementor-widget elementor-widget-text-editor\" data-id=\"3fe25988\" 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>Next comes the degree matrix (D). Based on the A matrix, it calculates the sum of the links (horizontal and vertical). Based on the previous example, here&#8217;s the result:<\/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-5a6262ef elementor-widget elementor-widget-html\" data-id=\"5a6262ef\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<table class=\"tftable\" border=\"1\">\r\n<tr><td>0<\/td><td>0<\/td><td>0<\/td><td>2<\/td><\/tr>\r\n<tr><td>0<\/td><td>3<\/td><td>0<\/td><td>0<\/td><\/tr>\r\n<tr><td>0<\/td><td>0<\/td><td>1<\/td><td>0<\/td><\/tr>\r\n<tr><td>1<\/td><td>0<\/td><td>0<\/td><td>0<\/td><\/tr>\r\n<\/table>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7a5482c7 elementor-widget elementor-widget-text-editor\" data-id=\"7a5482c7\" 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>Finally, the Laplacian matrix (L) corresponds to the degree matrix minus the adjacent matrix, i.e. L=D-A.<\/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-3f39d245 elementor-widget elementor-widget-html\" data-id=\"3f39d245\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<table class=\"tftable\" border=\"1\">\r\n<tr><td>-1<\/td><td>0<\/td><td>0<\/td><td>-1<\/td><\/tr>\r\n<tr><td>0<\/td><td>2<\/td><td>-1<\/td><td>-1<\/td><\/tr>\r\n<tr><td>0<\/td><td>-1<\/td><td>1<\/td><td>0<\/td><\/tr>\r\n<tr><td>1<\/td><td>-1<\/td><td>0<\/td><td>0<\/td><\/tr>\r\n<\/table>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-20a2c4f7 elementor-widget elementor-widget-text-editor\" data-id=\"20a2c4f7\" 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;\">This matrix is then represented visually in the form of a graph, known as a Laplacian graph.<\/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-496076e6 elementor-widget elementor-widget-heading\" data-id=\"496076e6\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Vectors<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2db440eb elementor-widget elementor-widget-text-editor\" data-id=\"2db440eb\" 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>Visual representation of the Laplacian graph (A) requires knowledge of the eigenvalues (eigenvalues= \u03bb) and eigenvectors (eigenvectors=x).<\/p><p>Let Ax = \u03bbx<\/p><p>The eigenvalues and eigenvectors of a matrix can be found <a href=\"https:\/\/datascientest.com\/en\/numpy-the-python-library-in-data-science\">using numpy on Python.<\/a><\/p><p>Using these matrices and vectors, it is then possible to group several subgraphs together based on their similarities. One of the most common methods for Spectral Clustering will then be to apply a more classical Clustering algorithm such as the Kmeans method on the eigenvectors of this matrix<\/p><p>This method can be directly implemented using the <strong>Spectral Clustering module<\/strong> of the <a href=\"https:\/\/datascientest.com\/en\/scikit-learn-discover-the-python-library-dedicated-to-machine-learning\">Sklearn library<\/a>. The eigenvalues are used to determine the optimal number of clusters for partitioning your data.<\/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-49eae7f4 elementor-widget elementor-widget-text-editor\" data-id=\"49eae7f4\" 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 style=\"text-align: left;\"><strong>\ud83d\udca1Good to know:<\/strong> the number of sub-graphs (clusters) depends on the objective and the desired degree of targeting. From a macro point of view, it&#8217;s preferable to have a limited number of clusters for a better overall view. But from a micro point of view, it&#8217;s just as interesting to go into detail with as many sub-groups as possible.<\/p><\/blockquote>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-e4f0a1b e-flex e-con-boxed e-con\" data-id=\"e4f0a1b\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6ad30643 elementor-widget elementor-widget-heading\" data-id=\"6ad30643\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Why use spectral partitioning?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5b69a00e elementor-widget elementor-widget-text-editor\" data-id=\"5b69a00e\" 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>The use of <strong>spectral partitioning<\/strong> is absolutely essential for understanding your target and addressing it in a personalized way.<\/p><p>Let&#8217;s take the example of people who subscribe to a clothing brand (this is the basis of our adjacent matrix).<\/p><p>Just because they all share the same affinity doesn&#8217;t mean they all have the same profile.<\/p><p>Subscribers include :<\/p><ul><li>Loyal customers ;<\/li><li>Hesitant prospects;<\/li><li>Stylists looking for inspiration;<\/li><li>Marketing professionals monitoring the market;<br \/>etc.<\/li><\/ul><p>Spectral clustering then segments the data into smaller sub-groups.<\/p><p>For companies, knowing each sub-group enables them to adapt their communication and offer. It&#8217;s all about addressing the right people, at the right time and in the right way, to multiply your chances of converting.<\/p><p>And, as we saw earlier, politicians can also use this algorithm to convince their voters.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-6c81a6fb e-flex e-con-boxed e-con\" data-id=\"6c81a6fb\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5d17981c elementor-widget elementor-widget-heading\" data-id=\"5d17981c\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What are the advantages and disadvantages of spectral clustering?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5915640f elementor-widget elementor-widget-image\" data-id=\"5915640f\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"640\" height=\"354\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/10\/200528094346386326.png\" class=\"attachment-large size-large wp-image-172030\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/10\/200528094346386326.png 640w, https:\/\/datascientest.com\/en\/files\/2023\/10\/200528094346386326-300x166.png 300w\" sizes=\"(max-width: 640px) 100vw, 640px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\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-7a68e84b elementor-widget elementor-widget-text-editor\" data-id=\"7a68e84b\" 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>The undeniable advantage of spectral clustering is its ability to classify a set of non-convex data.<\/p><p>However, it&#8217;s a complex concept that requires not only a good understanding of how it works, but also, and above all, a mastery of linear algebra and spectral graph theory.<\/p><p>That said, the Python-based numpy tool does the calculations, simplifying the analyst&#8217;s work.https:\/\/datascientest.com\/en\/python-all-you-need-to-know<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-5c2e5f0a e-flex e-con-boxed e-con\" data-id=\"5c2e5f0a\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3030e210 elementor-widget elementor-widget-heading\" data-id=\"3030e210\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">How do I learn about spectral clustering?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7bc5ec1e elementor-widget elementor-widget-text-editor\" data-id=\"7bc5ec1e\" 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>Since spectral clustering is particularly complex, it&#8217;s essential to get the right training to understand how it works.<br \/>An excellent training course in data science will enable you to master the tools needed to implement this algorithm.<\/p><p>You&#8217;ll then be able to use these skills in companies or public institutions to 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