{"id":196410,"date":"2025-07-15T06:29:00","date_gmt":"2025-07-15T05:29:00","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=196410"},"modified":"2025-07-11T17:31:29","modified_gmt":"2025-07-11T16:31:29","slug":"all-about-self-organizing-maps","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/all-about-self-organizing-maps","title":{"rendered":"Self-Organizing Maps (SOM): What are they and how to use them?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"196410\" class=\"elementor elementor-196410\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-9b21c4a e-flex e-con-boxed e-con\" data-id=\"9b21c4a\" 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-a71d132 elementor-widget elementor-widget-text-editor\" data-id=\"a71d132\" 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><b>Self-Organizing Maps, or SOM, represent a form of artificial neural network (ANN) employed for unsupervised learning. They facilitate the reduction of data dimensionality while retaining their topological structure, thus offering a robust tool for clustering and data exploration.<\/b><\/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-59ec481 elementor-widget elementor-widget-text-editor\" data-id=\"59ec481\" 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;\">Unlike traditional neural networks, self-organizing maps function via <\/span><b>competitive learning<\/b><span style=\"font-weight: 400;\"> as opposed to error correction. They incorporate a neighborhood function to preserve the <\/span><b>spatial relationships of the data<\/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-00fb5dc elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"00fb5dc\" 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\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\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\">More about SOMs<\/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\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-db2a58e e-flex e-con-boxed e-con\" data-id=\"db2a58e\" 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-580124b elementor-widget elementor-widget-heading\" data-id=\"580124b\" 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-xl\">Origin of SOMs<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d77bd91 elementor-widget elementor-widget-text-editor\" data-id=\"d77bd91\" 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;\">Self-organizing maps were introduced in the 1980s by the Finnish researcher <\/span><b>Teuvo Kohonen<\/b><span style=\"font-weight: 400;\">. This is why they are also referred to as <\/span><b>Kohonen maps<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">Inspired by biological brain mechanisms, they emulate how neurons organize and classify information, forming meaningful structures.<\/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\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-990970c e-flex e-con-boxed e-con\" data-id=\"990970c\" 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-178f019 elementor-widget elementor-widget-heading\" data-id=\"178f019\" 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-xl\">How SOM Works?<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a5c6d40 elementor-widget elementor-widget-text-editor\" data-id=\"a5c6d40\" 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;\">The learning process of a Self-Organizing Map relies on multiple steps that transform complex data into an <\/span><b>organized and readable representation<\/b><span style=\"font-weight: 400;\">. Below is a typical, step-by-step operation of a SOM.<\/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-e40afb2 elementor-widget elementor-widget-heading\" data-id=\"e40afb2\" 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\">1. Initialization of Weights<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7a7c062 elementor-widget elementor-widget-text-editor\" data-id=\"7a7c062\" 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;\">Before training starts, each neuron in the map is linked to a weight vector, which is initialized randomly. This vector shares the same dimension as the <\/span><b>input data<\/b><span style=\"font-weight: 400;\"> and embodies the identity of each neuron prior to adjustment through the learning process.<\/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-a77a814 elementor-widget elementor-widget-heading\" data-id=\"a77a814\" 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\">2. Selection of an Input Sample<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d8760f2 elementor-widget elementor-widget-text-editor\" data-id=\"d8760f2\" 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;\">During each iteration, an input vector is randomly selected from the <\/span><a href=\"https:\/\/datascientest.com\/en\/what-is-a-dataset-how-do-i-work-with-it\"><span style=\"font-weight: 400;\">training dataset<\/span><\/a><span style=\"font-weight: 400;\">. This vector signifies a <\/span><b>data point<\/b><span style=\"font-weight: 400;\"> that the SOM must learn to organize on the map.<\/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-c68a7f8 elementor-widget elementor-widget-heading\" data-id=\"c68a7f8\" 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\">3. Identification of the Best Matching Unit<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7d34429 elementor-widget elementor-widget-text-editor\" data-id=\"7d34429\" 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;\">Once the sample is chosen, the algorithm identifies the neuron whose weights are closest to this input vector. This proximity is determined using the <\/span><b>Euclidean distance<\/b><span style=\"font-weight: 400;\"> between the input vector and the neurons. The closest neuron is pinpointed as the <\/span><b>BMU (Best Matching Unit)<\/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-e92cf87 elementor-widget elementor-widget-image\" data-id=\"e92cf87\" 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=\"800\" height=\"457\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/05\/Self-Organizing-Maps-DataScientest-1.webp\" class=\"attachment-large size-large wp-image-196414\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/05\/Self-Organizing-Maps-DataScientest-1.webp 1000w, https:\/\/datascientest.com\/en\/files\/2025\/05\/Self-Organizing-Maps-DataScientest-1-300x171.webp 300w, https:\/\/datascientest.com\/en\/files\/2025\/05\/Self-Organizing-Maps-DataScientest-1-768x439.webp 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<div class=\"elementor-element elementor-element-464b1bc elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"464b1bc\" 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\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\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\">Mastering SOMs<\/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<div class=\"elementor-element elementor-element-d6608cb elementor-widget elementor-widget-heading\" data-id=\"d6608cb\" 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\">4. Updating the Weights of the BMU and Its Neighbors<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a00b1c4 elementor-widget elementor-widget-text-editor\" data-id=\"a00b1c4\" 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;\">After locating the BMU, the algorithm adjusts its weights to align more closely with the input vector. Neighboring neurons are also updated, albeit to a lesser extent.<\/span><\/p><p><span style=\"font-weight: 400;\">The extent of this update is influenced by two primary factors:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>The learning rate (denoted as \u03b1 or alpha) <\/b><span style=\"font-weight: 400;\">: Alpha governs the speed of adjustment for the neurons&#8217; weights. It diminishes over iterations to prevent abrupt changes.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>The neighborhood function <\/b><span style=\"font-weight: 400;\">: The update affects neurons around the BMU, with an impact that lessens with distance. A common choice is the Gaussian function.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bee687f elementor-widget elementor-widget-text-editor\" data-id=\"bee687f\" 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 phase enables the BMU and its neighbors to gradually align with the characteristics of the data while maintaining the topological integrity of the relationships between <\/span><b>data points<\/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-c6b5298 elementor-widget elementor-widget-heading\" data-id=\"c6b5298\" 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\">5. Reduction of the Learning Rate and Neighborhood<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-709f078 elementor-widget elementor-widget-text-editor\" data-id=\"709f078\" 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;\">As the iterations proceed, the <\/span><b>learning rate<\/b><span style=\"font-weight: 400;\"> and the <\/span><b>neighborhood size<\/b><span style=\"font-weight: 400;\"> decrease. This reduction allows for precise fine-tuning of weights in the final training stages and ensures effective organization of the data on the map.<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Initially, the neighborhood is extensive, enabling the entire map to organize globally.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Gradually, the neighborhood contracts, refining the map and stabilizing the formed clusters.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-dc5dad3 elementor-widget elementor-widget-heading\" data-id=\"dc5dad3\" 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\">6. Convergence and Stabilization<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a7c0bd8 elementor-widget elementor-widget-text-editor\" data-id=\"a7c0bd8\" 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;\">The training continues until the map achieves a stable state where the neurons&#8217; weights exhibit minimal change from one <\/span><b>iteration<\/b><span style=\"font-weight: 400;\"> to the next. In this stage, each neuron corresponds to a specific region of the input data.<\/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-640ae61 elementor-widget elementor-widget-heading\" data-id=\"640ae61\" 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\">7. Inference and Visualization of Results<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-86a8cd8 elementor-widget elementor-widget-text-editor\" data-id=\"86a8cd8\" 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;\">Once <\/span><b>the SOM is trained<\/b><span style=\"font-weight: 400;\">, it can organize new data and facilitate visual analysis. The distance between an <\/span><b>input vector<\/b><span style=\"font-weight: 400;\"> and the neurons&#8217; weights helps determine the positioning of new data on the map.<\/span><\/p><p><span style=\"font-weight: 400;\">A popular method for <\/span><b>visualizing SOMs<\/b><span style=\"font-weight: 400;\"> involves assigning colors to different map regions. Darker colors indicate a higher concentration of data.<\/span><\/p><p><span style=\"font-weight: 400;\">Clusters of similar data become apparent on the <\/span><b>map<\/b><span style=\"font-weight: 400;\">, providing an intuitive visualization of the interrelationships between various categories.<\/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-5eb8ec4 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"5eb8ec4\" 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\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\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\">Using SOMs in 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\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-d4faa9e e-flex e-con-boxed e-con\" data-id=\"d4faa9e\" 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-6f1cff9 elementor-widget elementor-widget-heading\" data-id=\"6f1cff9\" 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-xl\">Advantages and Disadvantages of SOM<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-36802f4 elementor-widget elementor-widget-text-editor\" data-id=\"36802f4\" 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;\">SOMs offer several significant advantages. They enable <\/span><b>dimensionality reduction<\/b><span style=\"font-weight: 400;\"> while maintaining topological organization. Their intuitive graphical representation aids the visualization and interpretation of complex datasets. They are commonly used for <\/span><b>clustering<\/b><span style=\"font-weight: 400;\">, even without prior knowledge of the data classes.<\/span><\/p><p><span style=\"font-weight: 400;\">However, SOMs have certain drawbacks. They do not adapt well to purely categorical or mixed data (except with proper encoding), which makes it challenging to follow a logical representation space. Their training time may be lengthy, and their effectiveness hinges on accurate parameter tuning.<\/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\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-d5681f7 e-flex e-con-boxed e-con\" data-id=\"d5681f7\" 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-789b43c elementor-widget elementor-widget-heading\" data-id=\"789b43c\" 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-xl\">Applications of SOM<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8a7631e elementor-widget elementor-widget-text-editor\" data-id=\"8a7631e\" 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;\">SOMs find application in diverse fields to <\/span><span style=\"font-weight: 400;\">organize and analyze data<\/span><span style=\"font-weight: 400;\">. For instance, in <\/span><a href=\"https:\/\/datascientest.com\/en\/all-about-big-data-marketing\"><span style=\"font-weight: 400;\">the marketing sector<\/span><\/a><span style=\"font-weight: 400;\">, they assist in grouping customers based on purchasing behavior to optimize <\/span><b>business strategies<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">For dimensionality reduction, they aid in the <\/span><b>mapping of high-dimensional data<\/b><span style=\"font-weight: 400;\">, facilitating a better understanding of internal data relationships.<\/span><\/p><p><span style=\"font-weight: 400;\">In <\/span><b>anomaly detection<\/b><span style=\"font-weight: 400;\">, they are utilized to identify fraudulent transactions by pinpointing data points that deviate from predefined clusters.<\/span><\/p><p><span style=\"font-weight: 400;\">For data visualization, they enhance understanding of populations and relationships between different parameters. By converting a complex dataset into a <\/span><b>2D representation<\/b><span style=\"font-weight: 400;\">, they allow for rapid identification of <\/span><b>trends<\/b><span style=\"font-weight: 400;\"> and <\/span><b>invisible patterns<\/b><span style=\"font-weight: 400;\"> in raw data tables.<\/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-a609dbc elementor-widget elementor-widget-image\" data-id=\"a609dbc\" 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=\"457\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/05\/Self-Organizing-Maps-DataScientest-2.webp\" class=\"attachment-large size-large wp-image-196413\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/05\/Self-Organizing-Maps-DataScientest-2.webp 1000w, https:\/\/datascientest.com\/en\/files\/2025\/05\/Self-Organizing-Maps-DataScientest-2-300x171.webp 300w, https:\/\/datascientest.com\/en\/files\/2025\/05\/Self-Organizing-Maps-DataScientest-2-768x439.webp 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\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-caa307f e-flex e-con-boxed e-con\" data-id=\"caa307f\" 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-3df9be3 elementor-widget elementor-widget-heading\" data-id=\"3df9be3\" 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-xl\">Conclusion<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-993c838 elementor-widget elementor-widget-text-editor\" data-id=\"993c838\" 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;\">SOMs are a formidable tool for unsupervised learning in <\/span><b>cluster analysis<\/b><span style=\"font-weight: 400;\">, <\/span><b>dimensionality reduction<\/b><span style=\"font-weight: 400;\">, and <\/span><b>data visualization<\/b><span style=\"font-weight: 400;\">. They do have limitations regarding training time and adaptation to mixed data. They are employed in fields such as finance, marketing, healthcare, and image analysis. Their capacity to unveil hidden structures in data renders them an indispensable choice for <\/span><b>exploring unlabeled data<\/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-7ed93e4 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"7ed93e4\" 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\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\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\">See our Data Science training courses<\/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\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ca1d170 e-flex e-con-boxed e-con\" data-id=\"ca1d170\" 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-6b165ed elementor-widget elementor-widget-html\" data-id=\"6b165ed\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\r\n{\r\n  \"@context\": \"https:\/\/schema.org\",\r\n  \"@type\": \"FAQPage\",\r\n  \"mainEntity\": [\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"Self-Organizing Maps (SOM): What is it? How to use it?\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"Self-Organizing Maps are a type of neural network used for unsupervised learning, reducing data dimensionality while preserving topological structure for clustering and exploration.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"Origin of SOM\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"Self-organizing maps were introduced in the 1980s by Finnish researcher Teuvo Kohonen and are inspired by biological mechanisms of brain function.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"How SOM Works\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"The learning process involves initializing weights, selecting input samples, identifying the Best Matching Unit, updating weights, and gradually reducing learning parameters until stabilization.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"Advantages and Disadvantages of SOM\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"SOMs reduce dimensionality and aid visualization but struggle with categorical data, require careful tuning, and can have long training times.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"Applications of SOM\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"They are used in marketing for customer segmentation, anomaly detection, dimensionality reduction, and data visualization to reveal patterns and trends.\"\r\n      }\r\n    }\r\n  ]\r\n}\r\n<\/script>\r\n\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\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Self-Organizing Maps, or SOM, represent a form of artificial neural network (ANN) employed for unsupervised learning. They facilitate the reduction of data dimensionality while retaining their topological structure, thus offering a robust tool for clustering and data exploration. Unlike traditional neural networks, self-organizing maps function via competitive learning as opposed to error correction. They incorporate [&hellip;]<\/p>\n","protected":false},"author":45,"featured_media":196412,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2427,2425],"tags":[],"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>Self-Organizing Maps (SOM): What are they and how to use them?<\/title>\n<meta name=\"description\" content=\"Discover Self-Organizing Maps (SOM), a powerful tool for unsupervised data exploration and visualization. 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