{"id":7685,"date":"2018-10-18T22:34:37","date_gmt":"2018-10-18T20:34:37","guid":{"rendered":"https:\/\/www.datanovia.com\/en\/?post_type=dt_courses&#038;p=7685"},"modified":"2018-10-20T18:40:11","modified_gmt":"2018-10-20T16:40:11","slug":"hierarchical-clustering-in-r-the-essentials","status":"publish","type":"dt_courses","link":"https:\/\/www.datanovia.com\/en\/courses\/hierarchical-clustering-in-r-the-essentials\/","title":{"rendered":"Hierarchical Clustering in R: The Essentials"},"content":{"rendered":"<div id=\"rdoc\">\n<p>The <strong>Hierarchical clustering<\/strong> [or <strong>hierarchical cluster analysis<\/strong> (<strong>HCA<\/strong>)] method is an alternative approach to <a href=\"https:\/\/www.datanovia.com\/en\/courses\/partitional-clustering-in-r-the-essentials\/\">partitional clustering<\/a> for grouping objects based on their similarity.<\/p>\n<p>In contrast to partitional clustering, the hierarchical clustering does not require to pre-specify the number of clusters to be produced.<\/p>\n<p>Hierarchical clustering can be subdivided into two types:<\/p>\n<ul>\n<li><em>Agglomerative clustering<\/em> in which, each observation is initially considered as a cluster of its own (leaf). Then, the most similar clusters are successively merged until there is just one single big cluster (root).<\/li>\n<li><em>Divise clustering<\/em>, an inverse of agglomerative clustering, begins with the root, in witch all objects are included in one cluster. Then the most heterogeneous clusters are successively divided until all observation are in their own cluster.<\/li>\n<\/ul>\n<p>The result of hierarchical clustering is a tree-based representation of the objects, which is also known as <em>dendrogram<\/em> (see the figure below).<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/003-hierarchical-clustering-in-r\/figures\/001-hierarchical-clustering-in-r-intro-dendrogram-hclust-1.png\" width=\"518.4\" \/><\/p>\n<p>The dendrogram is a multilevel hierarchy where clusters at one level are joined together to form the clusters at the next levels. This makes it possible to decide the level at which to cut the tree for generating suitable groups of a data objects.<\/p>\n<div class=\"block\">\n<p>In this course, you will learn:<\/p>\n<ul>\n<li>The hierarchical clustering algorithms<\/li>\n<li>Examples of computing and visualizing hierarchical clustering in R<\/li>\n<li>How to cut dendrograms into groups.<\/li>\n<li>How to compare two dendrograms.<\/li>\n<li>Solutions for handling dendrograms of large data sets.<\/li>\n<\/ul>\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>\n<p><!--end rdoc--><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Hierarchical clustering is an unsupervised machine learning method used to classify objects into groups based on their similarity. In this course, you will learn the algorithm and practical examples in R. We&#8217;ll also show  how to cut dendrograms into groups and to compare two dendrograms. Finally, you will learn how to zoom a large dendrogram.<\/p>\n","protected":false},"author":1,"featured_media":8008,"menu_order":52,"comment_status":"open","ping_status":"closed","template":"","class_list":["post-7685","dt_courses","type-dt_courses","status-publish","has-post-thumbnail","hentry","course_category-cluster-analysis-in-r"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Hierarchical Clustering in R: The Essentials - Datanovia<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.datanovia.com\/en\/courses\/hierarchical-clustering-in-r-the-essentials\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Hierarchical Clustering in R: The Essentials - Datanovia\" \/>\n<meta property=\"og:description\" content=\"Hierarchical clustering is an unsupervised machine learning method used to classify objects into groups based on their similarity. In this course, you will learn the algorithm and practical examples in R. We&#039;ll also show how to cut dendrograms into groups and to compare two dendrograms. Finally, you will learn how to zoom a large dendrogram.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.datanovia.com\/en\/courses\/hierarchical-clustering-in-r-the-essentials\/\" \/>\n<meta property=\"og:site_name\" content=\"Datanovia\" \/>\n<meta property=\"article:modified_time\" content=\"2018-10-20T16:40:11+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2018\/10\/IMG_0227-1.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1024\" \/>\n\t<meta property=\"og:image:height\" content=\"512\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.datanovia.com\/en\/courses\/hierarchical-clustering-in-r-the-essentials\/\",\"url\":\"https:\/\/www.datanovia.com\/en\/courses\/hierarchical-clustering-in-r-the-essentials\/\",\"name\":\"Hierarchical Clustering in R: The Essentials - Datanovia\",\"isPartOf\":{\"@id\":\"https:\/\/www.datanovia.com\/en\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/www.datanovia.com\/en\/courses\/hierarchical-clustering-in-r-the-essentials\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/www.datanovia.com\/en\/courses\/hierarchical-clustering-in-r-the-essentials\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2018\/10\/IMG_0227-1.jpg\",\"datePublished\":\"2018-10-18T20:34:37+00:00\",\"dateModified\":\"2018-10-20T16:40:11+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/www.datanovia.com\/en\/courses\/hierarchical-clustering-in-r-the-essentials\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.datanovia.com\/en\/courses\/hierarchical-clustering-in-r-the-essentials\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.datanovia.com\/en\/courses\/hierarchical-clustering-in-r-the-essentials\/#primaryimage\",\"url\":\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2018\/10\/IMG_0227-1.jpg\",\"contentUrl\":\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2018\/10\/IMG_0227-1.jpg\",\"width\":1024,\"height\":512},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.datanovia.com\/en\/courses\/hierarchical-clustering-in-r-the-essentials\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/www.datanovia.com\/en\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Courses\",\"item\":\"https:\/\/www.datanovia.com\/en\/courses\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Hierarchical Clustering in R: The Essentials\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.datanovia.com\/en\/#website\",\"url\":\"https:\/\/www.datanovia.com\/en\/\",\"name\":\"Datanovia\",\"description\":\"Data Mining and Statistics for Decision Support\",\"publisher\":{\"@id\":\"https:\/\/www.datanovia.com\/en\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.datanovia.com\/en\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/www.datanovia.com\/en\/#organization\",\"name\":\"Datanovia\",\"url\":\"https:\/\/www.datanovia.com\/en\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.datanovia.com\/en\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2018\/09\/datanovia-logo.png\",\"contentUrl\":\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2018\/09\/datanovia-logo.png\",\"width\":98,\"height\":99,\"caption\":\"Datanovia\"},\"image\":{\"@id\":\"https:\/\/www.datanovia.com\/en\/#\/schema\/logo\/image\/\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Hierarchical Clustering in R: The Essentials - Datanovia","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.datanovia.com\/en\/courses\/hierarchical-clustering-in-r-the-essentials\/","og_locale":"en_US","og_type":"article","og_title":"Hierarchical Clustering in R: The Essentials - Datanovia","og_description":"Hierarchical clustering is an unsupervised machine learning method used to classify objects into groups based on their similarity. 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