{"id":8149,"date":"2018-11-06T21:03:16","date_gmt":"2018-11-06T19:03:16","guid":{"rendered":"https:\/\/www.datanovia.com\/en\/?p=8149"},"modified":"2025-03-22T21:25:59","modified_gmt":"2025-03-22T20:25:59","slug":"cluster-analysis-in-r-practical-guide","status":"publish","type":"post","link":"https:\/\/www.datanovia.com\/en\/blog\/cluster-analysis-in-r-practical-guide\/","title":{"rendered":"Cluster Analysis in R: Practical Guide"},"content":{"rendered":"<div id=\"rdoc\">\n<p><strong>Cluster analysis<\/strong> is one of the important data mining methods for discovering knowledge in multidimensional data. The goal of clustering is to identify pattern or groups of similar objects within a data set of interest.<\/p>\n<p>Each group contains observations with similar profile according to a specific criteria. Similarity between observations is defined using some inter-observation distance measures including Euclidean and correlation-based distance measures.<\/p>\n<p>In the literature, cluster analysis is referred as \u201cpattern recognition\u201d or \u201c<em>unsupervised machine learning<\/em>\u201d - \u201cunsupervised\u201d because we are not guided by a priori ideas of which variables or samples belong in which clusters. \u201cLearning\u201d because the machine algorithm \u201clearns\u201d how to cluster.<\/p>\n<p>Cluster analysis is popular in many fields, including:<\/p>\n<ul>\n<li>In cancer research, for classifying patients into subgroups according their gene expression profile. This can be useful for identifying the molecular profile of patients with good or bad prognostic, as well as for understanding the disease.<\/li>\n<li>In marketing, for market segmentation by identifying subgroups of customers with similar profiles and who might be receptive to a particular form of advertising.<\/li>\n<li>In City-planning, for identifying groups of houses according to their type, value and location.<\/li>\n<\/ul>\n<p>Note that, it\u2019 possible to cluster both observations (i.e, samples or individuals) and features (i.e, variables). Observations can be clustered on the basis of variables and variables can be clustered on the basis of observations.<\/p>\n<div class=\"block\">\n<p>Here, we provide a practical guide to unsupervised machine learning or cluster analysis using R software.<\/p>\n<\/div>\n<div class='dt-sc-hr-invisible-medium  '><\/div>\n<div class='dt-sc-ico-content type1'><div class='custom-icon' ><a href='https:\/\/www.datanovia.com\/en\/product\/practical-guide-to-cluster-analysis-in-r\/' target='_blank'><span class='fa fa-book'><\/span><\/a><\/div><h4><a href='https:\/\/www.datanovia.com\/en\/product\/practical-guide-to-cluster-analysis-in-r\/' target='_blank'> Related Book <\/a><\/h4>Practical Guide to Cluster Analysis in R<\/div>\n<div class='dt-sc-hr-invisible-medium  '><\/div>\n<div id=\"how-this-document-is-organized\" class=\"section level2\">\n<h2>How this document is organized??<\/h2>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/cluster-analysis\/images\/clustering-plan.png\" alt=\"clustering plan\" \/><\/p>\n<p>This document contains 5 parts.<\/p>\n<p><a href=\"\/?p=7641\"><i class=\"fa fa-file-text\"> <\/i> Part I. Cluster Analysis Basics<\/a>:<\/p>\n<ul>\n<li>Data Preparation and Essential R Packages for Cluster Analysis<\/li>\n<li>Clustering Distance Measures Essentials<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><a href=\"https:\/\/www.datanovia.com\/en\/courses\/data-clustering-basics\/\"><i class=\"fa fa-file-text\"> <\/i> Part II. Partitional Clustering methods<\/a>:<\/p>\n<ul>\n<li>K-Means Clustering Essentials<\/li>\n<li>K-Medoids Essentials: PAM clustering<\/li>\n<li>CLARA - Clustering Large Applications<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><a href=\"https:\/\/www.datanovia.com\/en\/courses\/hierarchical-clustering-in-r-the-essentials\/\"><i class=\"fa fa-file-text\"> <\/i> Part III. Hierarchical Clustering<\/a>:<\/p>\n<ul>\n<li>Agglomerative Clustering\n<ul>\n<li>Algorithm and steps<\/li>\n<li>Verify the cluster tree<\/li>\n<li>Cut the dendrogram into different groups<\/li>\n<\/ul>\n<\/li>\n<li>Divisive Clustering<\/li>\n<li>Compare Dendrograms\n<ul>\n<li>Visual comparison of two dendrograms<\/li>\n<li>Correlation matrix between a list of dendrograms<\/li>\n<\/ul>\n<\/li>\n<li>Visualize Dendrograms\n<ul>\n<li>Case of small data sets<\/li>\n<li>Case of dendrogram with large data sets: zoom, sub-tree, PDF<\/li>\n<li>Customize dendrograms using dendextend<\/li>\n<\/ul>\n<\/li>\n<li>Heatmap: Static and Interactive\n<ul>\n<li>R base heat maps<\/li>\n<li>Pretty heat maps<\/li>\n<li>Interactive heat maps<\/li>\n<li>Complex heatmap<\/li>\n<li>Real application: gene expression data<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><a href=\"\/?p=8058\"><i class=\"fa fa-file-text\"> <\/i> Part IV. Clustering Validation and Evaluation Strategies<\/a> :<\/p>\n<ul>\n<li>Assessing Clustering Tendency<\/li>\n<li>Determining the Optimal Number of Clusters<\/li>\n<li>Cluster Validation Statistics<\/li>\n<li>Choosing the Best Clustering Algorithms<\/li>\n<li>Computing p-value for Hierarchical Clustering<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><a href=\"\/?p=8077\"><i class=\"fa fa-file-text\"> <\/i> Part V. Advanced Clustering<\/a>:<\/p>\n<ul>\n<li>Hierarchical K-means Clustering<\/li>\n<li>Fuzzy Clustering<\/li>\n<li>Model-Based Clustering<\/li>\n<li>DBSCAN: Density-Based Clustering<\/li>\n<\/ul>\n<\/div>\n<div id=\"related-blog-posts\" class=\"section level2\">\n<h2>Related blog posts<\/h2>\n<ul>\n<li><a href=\"https:\/\/www.datanovia.com\/en\/blog\/cluster-analysis-in-r-simplified-and-enhanced\/\">Cluster Analysis in R Simplified and Enhanced<\/a><\/li>\n<li><a href=\"https:\/\/www.datanovia.com\/en\/blog\/clustering-example-4-steps-you-should-know\/\">Clustering Example: 4 Steps You Should Know<\/a><\/li>\n<li><a href=\"https:\/\/www.datanovia.com\/en\/blog\/types-of-clustering-methods-overview-and-quick-start-r-code\/\">Types of Clustering Methods: Overview and Quick Start R Code<\/a><\/li>\n<\/ul>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Cluster analysis is one of the important data mining methods for discovering knowledge in multidimensional data. The goal of clustering is to identify pattern or groups of similar objects within [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":7812,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"rating_form_position":"","rating_results_position":"","mr_structured_data_type":"","footnotes":""},"categories":[123],"tags":[],"class_list":["post-8149","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-cluster-analysis"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>The Ultimate Guide to Cluster Analysis in R - Datanovia<\/title>\n<meta name=\"description\" content=\"This article provides a practical guide to cluster analysis in R. 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