{"id":8158,"date":"2018-02-15T09:54:27","date_gmt":"2018-02-15T09:54:27","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=8158"},"modified":"2025-07-27T19:08:39","modified_gmt":"2025-07-27T13:38:39","slug":"clustering-in-data-mining","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/clustering-in-data-mining\/","title":{"rendered":"Clustering in Data Mining &#8211; Algorithms of Cluster Analysis in Data Mining"},"content":{"rendered":"<div>\n<div class=\"\">\n<p>In this blog, we will study Cluster Analysis in<strong> Data Mining<\/strong>. First, we will study clustering in data mining and the introduction and requirements of\u00a0clustering in Data mining.<\/p>\n<p>Moreover, we will discuss the applications &amp; algorithm of Cluster Analysis in Data Mining. Further, we will cover Data Mining Clustering Methods and approaches to Cluster Analysis.<\/p>\n<p>So, let&#8217;s start exploring Clustering in Data Mining.<\/p>\n<\/div>\n<\/div>\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Introduction to Cluster Analysis<\/h3>\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">a. What is Clustering in Data Mining?<\/h4>\n<div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Generally, a group of abstract objects into classes of similar objects <span class=\"passivevoice\">is made<\/span>.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">We treat a cluster of data objects as one group.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">While doing cluster analysis, we first partition the set of data into groups. That based on data similarity and then assign the labels to the groups.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">The main advantage of over-classification is that it is adaptable to changes. And helps single out useful features that distinguish different groups.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">b. What is Cluster Analysis in Data Mining?<\/h4>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Finding groups of objects such that the objects in a group will be like one another. And different from the objects in other groups.<\/div>\n<\/div>\n<div class=\"\">\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Applications of Data Mining Cluster Analysis<\/h3>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Data Clustering analysis <span class=\"passivevoice\">is used<\/span> in many applications. Such as market research, pattern recognition, data analysis, and image processing.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Data Clustering can also help marketers discover distinct groups in their customer base. And they can characterize their customer groups based on the purchasing patterns.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">In the field of biology, it can <span class=\"passivevoice\">be used<\/span> to derive plant and animal taxonomies. categorize genes with similar functionalities and gain insight into structures inherent to populations.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Clustering in Data Mining helps in identification of areas. That is of similar land use in an earth observation database. It also helps in the identification of groups of houses in a city. That is according to house type, value, and geographic location.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Clustering\u00a0in Data\u00a0Mining\u00a0also helps in classifying documents on the web for information discovery<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Also, we use Data clustering in outlier detection applications. Such as detection of credit card fraud.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">As a data mining function, cluster analysis serves as a tool. That is to gain insight into the distribution of data. Also, need to observe characteristics of each cluster.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Requirements of Clustering in Data Mining<\/h3>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">The following points state us the requirement of clustering in Data Mining:<\/div>\n<\/div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">a. Scalability<\/h4>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">We need <span class=\"adverb\">highly<\/span> scalable clustering algorithms to deal with large databases.<\/div>\n<\/div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">b. Ability to deal with different kinds of attributes<\/h4>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Algorithms should be capable to <span class=\"passivevoice\">be applied<\/span> to any kind of data. Such as interval-based data, categorical, and binary data.<\/div>\n<\/div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">c. Discovery of clusters with attribute shape<\/h4>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">The clustering algorithm should be capable of detecting clusters of arbitrary shape. They should not <span class=\"passivevoice\">be bounded<\/span> by only distance measures. That tends to find a spherical cluster of small sizes.<\/div>\n<\/div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">d. High dimensionality<\/h4>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">The clustering algorithm should not only be able to handle low-dimensional data. Although, need to handle the high dimensional space.<\/div>\n<\/div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">e. Ability to deal with noisy data<\/h4>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Databases contain noisy, missing or erroneous data. Some algorithms are sensitive to such data and may lead to poor quality clusters.<\/div>\n<\/div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">f. Interpretability<\/h4>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">The clustering results should be interpretable, comprehensible, and usable.<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Data Mining Clustering Methods<\/h3>\n<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><span class=\"passivevoice\">Data Mining Clustering Methods are classified<\/span> into the following categories \u2212<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">\n<div id=\"attachment_8426\" style=\"width: 1210px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/Clustering-Methods-01-1.jpg\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-8426\" class=\"wp-image-8426 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/Clustering-Methods-01-1.jpg\" alt=\"Clustering in Data Mining - Clustering Methods\" width=\"1200\" height=\"628\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/Clustering-Methods-01-1.jpg 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/Clustering-Methods-01-1-150x79.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/Clustering-Methods-01-1-300x157.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/Clustering-Methods-01-1-768x402.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/Clustering-Methods-01-1-1024x536.jpg 1024w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><p id=\"caption-attachment-8426\" class=\"wp-caption-text\">Clustering in Data Mining &#8211; Clustering Methods<\/p><\/div>\n<\/div>\n<\/div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">a. Partitioning Clustering Method<\/h4>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Suppose we <span class=\"passivevoice\">are given<\/span> a database of \u2018n\u2019 objects. And the partitioning method constructs \u2018k\u2019 partition of data. Each partition will represent a cluster and k \u2264 n. It means that it will classify the data into k groups. That must need to <span class=\"complexword\">satisfy<\/span> the following requirements \u2212<\/div>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Each group contains at least one object.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Each object must belong to exactly one group.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong><em>Points to remember \u2212<\/em><\/strong><\/div>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">If we have a given number of partitions (say k). Then the partitioning method will create an initial partitioning.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Further, it uses the iterative relocation technique. That is to improve the partitioning by moving objects from one group to other.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">b. Hierarchical\u00a0Clustering Methods<\/h4>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">The hierarchical method creates a hierarchical decomposition of the given set of data objects. We can classify methods on the basis of how the hierarchical decomposition <span class=\"passivevoice\">is formed<\/span>. There are two approaches here \u2212<\/div>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Agglomerative Approach<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Divisive Approach<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<h5 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">i. Agglomerative Approach<\/h5>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">This approach is also known as the bottom-up approach. In this, we start with each object forming a separate group. It keeps on merging the objects or groups that are close to one another.<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">It keeps on doing so until <span class=\"complexword\">all of<\/span> the groups <span class=\"passivevoice\">are merged<\/span> into one or until the termination condition holds.<strong><em>\u00a0<\/em><\/strong><\/div>\n<\/div>\n<div class=\"\">\n<h5 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">ii. Divisive Approach<\/h5>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">This approach is also known as the top-down approach. In this, we start with <span class=\"complexword\">all of<\/span> the objects in the same cluster. Then, in the continuous iteration, a cluster <span class=\"passivevoice\">is split<\/span> up into smaller clusters. Also, it is down until each object in one cluster or the termination condition holds.<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Hence, this method is rigid, i.e., once a merging or splitting <span class=\"passivevoice\">is done<\/span>, it can never be undone.<\/div>\n<div><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>Approaches to Improve Quality of Hierarchical Clustering in Data Mining<\/strong><\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Here are the two approaches. That we used to improve the quality of hierarchical clustering in Data Mining\u2212<\/div>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Perform careful analysis of object linkages at each hierarchical partitioning.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Integrate hierarchical agglomeration by using a hierarchical agglomerative algorithm. Then to group objects into micro-clusters, and then performing macro-clustering on the micro-clusters.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">c. Density-Based\u00a0Clustering Method<\/h4>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">This Data Mining Clustering method <span class=\"passivevoice\">is based<\/span> on the notion of density. The idea is to continue growing the given cluster. That is exceeding as long as the density in the neighbourhood threshold.<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">For each data point within a given cluster, the radius of a given cluster has to contain at least number of points.<\/div>\n<\/div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">d. Grid-Based Clustering Method<\/h4>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">In this, the objects together form a grid. The object space <span class=\"passivevoice\">is quantized<\/span> into a finite number of cells that form a grid structure.<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>Advantages<\/strong><\/div>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">The major advantage of this method is a fast processing time.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">It is dependent only on the number of cells in each dimension in the quantized space.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">e. Model-Based Clustering Methods<\/h4>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">In this Data Mining Clustering method, a model <span class=\"passivevoice\">is hypothesized<\/span> for each cluster to find the best fit of data for a given model. Also, this method locates the clusters by clustering the density function.<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Thus, it reflects the spatial distribution of the data points.<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">This method also provides a way to determine the number of clusters. That <span class=\"passivevoice\">was based<\/span> on standard statistics, taking outlier or noise into account. It, <span class=\"complexword\">therefore<\/span>, yields robust clustering methods.<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">f. Constraint-Based Clustering Method<\/h4>\n<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">The clustering <span class=\"passivevoice\">is performed by<\/span> the incorporation of a user or application-oriented constraints. A constraint refers to the user expectation. Constraints provide us with an interactive way of communication with the clustering process. Constraints can <span class=\"passivevoice\">be specified by<\/span> the user or the application need.<\/div>\n<\/div>\n<div class=\"\">\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">What is Not Cluster Analysis?<\/h3>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>Supervised classification &#8211;<\/strong> Have class label information<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>Simple segmentation &#8211;<\/strong> Dividing students into different registration groups, by the last name<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>Results of a query &#8211;<\/strong> Basically, groupings are a result of an external specification<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>Graph partitioning &#8211;<\/strong> Some mutual relevance and synergy, but areas are not identical<\/li>\n<\/ul>\n<p>So, this was all about Clustering in Data Mining. Hope you like our explanation.<\/p>\n<\/div>\n<div class=\"\">\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Conclusion<\/h3>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Clustering is a technique used in data mining to group similar data points together. It helps find patterns that are not visible at first. For example, in a shopping mall, clustering can help group customers who buy similar products. This helps the mall give better offers. Unlike classification, clustering does not need labeled data. It is a type of unsupervised learning.<\/div>\n<div><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">The most common clustering algorithms are K-Means, Hierarchical Clustering, and DBSCAN. K-Means divides data into K groups based on similarity. Hierarchical builds a tree of clusters by joining or splitting them. DBSCAN groups data based on density and can find outliers easily. These methods are useful in market research, image processing, and social network analysis.<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>In this blog, we will study Cluster Analysis in Data Mining. First, we will study clustering in data mining and the introduction and requirements of\u00a0clustering in Data mining. Moreover, we will discuss the applications&#46;&#46;&#46;<\/p>\n","protected":false},"author":6,"featured_media":8368,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[18,36],"tags":[389,2609,2613,3345,3753,5630,7012,15664,15666],"class_list":["post-8158","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-mining","category-machine-learning","tag-agglomerative-approach","tag-clustering-in-data-mining","tag-clustering-methods","tag-data-mining-cluster-analysis","tag-density-based-method","tag-hierarchical-clustering-methods","tag-introduction-to-cluster-analysis","tag-what-is-cluster-analysis","tag-what-is-clustering-in-data-mining"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Clustering in Data Mining - Algorithms of Cluster Analysis in Data Mining - DataFlair<\/title>\n<meta name=\"description\" content=\"Clustering in data mining,Application &amp; Requirements of Cluster analysis in data mining,Clustering Methods,Requirements &amp;\u00a0Applications of Cluster Analysis\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/clustering-in-data-mining\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Clustering in Data Mining - Algorithms of Cluster Analysis in Data Mining - DataFlair\" \/>\n<meta property=\"og:description\" content=\"Clustering in data mining,Application &amp; Requirements of Cluster analysis in data mining,Clustering Methods,Requirements &amp;\u00a0Applications of Cluster Analysis\" \/>\n<meta property=\"og:url\" content=\"https:\/\/data-flair.training\/blogs\/clustering-in-data-mining\/\" \/>\n<meta property=\"og:site_name\" content=\"DataFlair\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/DataFlairWS\/\" \/>\n<meta property=\"article:published_time\" content=\"2018-02-15T09:54:27+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-07-27T13:38:39+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/Introduction-to-Data-Mining-Cluster-Analysis-01-1.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"628\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"DataFlair Team\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DataFlairWS\" \/>\n<meta name=\"twitter:site\" content=\"@DataFlairWS\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"DataFlair Team\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"7 minutes\" \/>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Clustering in Data Mining - Algorithms of Cluster Analysis in Data Mining - DataFlair","description":"Clustering in data mining,Application & Requirements of Cluster analysis in data mining,Clustering Methods,Requirements &\u00a0Applications of Cluster Analysis","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:\/\/data-flair.training\/blogs\/clustering-in-data-mining\/","og_locale":"en_US","og_type":"article","og_title":"Clustering in Data Mining - Algorithms of Cluster Analysis in Data Mining - DataFlair","og_description":"Clustering in data mining,Application & Requirements of Cluster analysis in data mining,Clustering Methods,Requirements &\u00a0Applications of Cluster Analysis","og_url":"https:\/\/data-flair.training\/blogs\/clustering-in-data-mining\/","og_site_name":"DataFlair","article_publisher":"https:\/\/www.facebook.com\/DataFlairWS\/","article_published_time":"2018-02-15T09:54:27+00:00","article_modified_time":"2025-07-27T13:38:39+00:00","og_image":[{"width":1200,"height":628,"url":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/Introduction-to-Data-Mining-Cluster-Analysis-01-1.jpg","type":"image\/jpeg"}],"author":"DataFlair Team","twitter_card":"summary_large_image","twitter_creator":"@DataFlairWS","twitter_site":"@DataFlairWS","twitter_misc":{"Written by":"DataFlair Team","Est. reading time":"7 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/data-flair.training\/blogs\/clustering-in-data-mining\/#article","isPartOf":{"@id":"https:\/\/data-flair.training\/blogs\/clustering-in-data-mining\/"},"author":{"name":"DataFlair Team","@id":"https:\/\/data-flair.training\/blogs\/#\/schema\/person\/2c58ecb4f73a39f0ef993f1ddfcd7b89"},"headline":"Clustering in Data Mining &#8211; 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