{"id":875,"date":"2016-09-24T14:06:52","date_gmt":"2016-09-24T14:06:52","guid":{"rendered":"http:\/\/dataaspirant.com\/?p=875"},"modified":"2016-09-25T04:09:44","modified_gmt":"2016-09-25T04:09:44","slug":"classification-clustering-alogrithms","status":"publish","type":"post","link":"https:\/\/dataaspirant.com\/classification-clustering-alogrithms\/","title":{"rendered":"classification and clustering algorithms"},"content":{"rendered":"<div id=\"bsf_rt_marker\"><\/div><div id=\"attachment_876\" style=\"width: 1034px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-876\" class=\"wp-image-876 size-large lazyload\" title=\"Classification and Clustering Algorithms\" data-src=\"http:\/\/dataaspirant.com\/wp-content\/uploads\/2016\/09\/Classification-and-Clustering-1024x768.png\" alt=\"classification and clustering algorithms\" width=\"1024\" height=\"768\" data-srcset=\"https:\/\/dataaspirant.com\/wp-content\/uploads\/2016\/09\/Classification-and-Clustering.png 1024w, https:\/\/dataaspirant.com\/wp-content\/uploads\/2016\/09\/Classification-and-Clustering-300x225.png 300w, https:\/\/dataaspirant.com\/wp-content\/uploads\/2016\/09\/Classification-and-Clustering-768x576.png 768w\" data-sizes=\"(max-width: 1024px) 100vw, 1024px\" src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" style=\"--smush-placeholder-width: 1024px; --smush-placeholder-aspect-ratio: 1024\/768;\" \/><noscript><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-876\" class=\"wp-image-876 size-large\" title=\"Classification and Clustering Algorithms\" src=\"http:\/\/dataaspirant.com\/wp-content\/uploads\/2016\/09\/Classification-and-Clustering-1024x768.png\" alt=\"classification and clustering algorithms\" width=\"1024\" height=\"768\" srcset=\"https:\/\/dataaspirant.com\/wp-content\/uploads\/2016\/09\/Classification-and-Clustering.png 1024w, https:\/\/dataaspirant.com\/wp-content\/uploads\/2016\/09\/Classification-and-Clustering-300x225.png 300w, https:\/\/dataaspirant.com\/wp-content\/uploads\/2016\/09\/Classification-and-Clustering-768x576.png 768w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/noscript><p id=\"caption-attachment-876\" class=\"wp-caption-text\"><strong>Classification and Clustering Algorithms<\/strong><\/p><\/div>\n<blockquote><p>Solving real world problems with data science concepts is so exciting and it yields so fun.<\/p><\/blockquote>\n<p>A famous dialogue you could listen from the\u00a0data science people. It could be true if we add it&#8217;s so challenging\u00a0at the end of the dialogue. The foremost challenge starts from \u00a0categorising the problem itself. The first level of categorising could be whether <a href=\"http:\/\/dataaspirant.com\/2014\/09\/19\/supervised-and-unsupervised-learning\/\" target=\"_blank\">supervised or unsupervised learning<\/a>. The next level is what kind of algorithms to get start with whether\u00a0to start with classification algorithms or with clustering algorithms?<\/p>\n<p>As we have covered the first level of categorising <a href=\"http:\/\/dataaspirant.com\/2014\/09\/19\/supervised-and-unsupervised-learning\/\" target=\"_blank\">supervised and unsupervised\u00a0learning<\/a>\u00a0in our previous post, now we would like to address the key differences between classification and clustering algorithms. First of all, it&#8217;s better to know the differences between \u00a0<a href=\"http:\/\/dataaspirant.com\/2014\/09\/27\/classification-and-prediction\/\" target=\"_blank\">classification and prediction<\/a> before knowing the difference between classification and clustering.<\/p>\n<p>Now Let&#8217;s begin with classification concepts.<\/p>\n<h2>Classification Concept<\/h2>\n<div id=\"attachment_890\" style=\"width: 606px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-890\" class=\"wp-image-890 lazyload\" data-src=\"http:\/\/dataaspirant.com\/wp-content\/uploads\/2016\/09\/Classification-1.png\" alt=\"Classification Concepts\" width=\"596\" height=\"500\" data-srcset=\"https:\/\/dataaspirant.com\/wp-content\/uploads\/2016\/09\/Classification-1.png 940w, https:\/\/dataaspirant.com\/wp-content\/uploads\/2016\/09\/Classification-1-300x251.png 300w, https:\/\/dataaspirant.com\/wp-content\/uploads\/2016\/09\/Classification-1-768x644.png 768w\" data-sizes=\"(max-width: 596px) 100vw, 596px\" src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" style=\"--smush-placeholder-width: 596px; --smush-placeholder-aspect-ratio: 596\/500;\" \/><noscript><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-890\" class=\"wp-image-890\" src=\"http:\/\/dataaspirant.com\/wp-content\/uploads\/2016\/09\/Classification-1.png\" alt=\"Classification Concepts\" width=\"596\" height=\"500\" srcset=\"https:\/\/dataaspirant.com\/wp-content\/uploads\/2016\/09\/Classification-1.png 940w, https:\/\/dataaspirant.com\/wp-content\/uploads\/2016\/09\/Classification-1-300x251.png 300w, https:\/\/dataaspirant.com\/wp-content\/uploads\/2016\/09\/Classification-1-768x644.png 768w\" sizes=\"(max-width: 596px) 100vw, 596px\" \/><\/noscript><p id=\"caption-attachment-890\" class=\"wp-caption-text\"><strong>Classification Concepts<\/strong><\/p><\/div>\n<p>In classification, the idea is to predict the target class by analysis the training dataset. This could be done by finding proper boundaries\u00a0for each target class. In a general way of saying, Use the training dataset to get better boundary conditions which could be used\u00a0to determine each target class. Once the boundary conditions determined, the next task is to predict the target class as we have said earlier. The whole process is known as classification.<\/p>\n<h3>Target class examples:<\/h3>\n<ul>\n<li>Analysis the customer\u00a0data to predict whether he will by computer accessories (Target class: Yes or No)<\/li>\n<li>Gender classification from hair length (Target classes: Male or Female)<\/li>\n<li>Classifying\u00a0fruits\u00a0from each fruit feature like colour, taste, size, weight (Targe class: Apple, Orange, Cherry, Banana)<\/li>\n<\/ul>\n<p>Let&#8217;s understand the concept of classification with gender classification using hair length. To classify gender (target class) using hair length as feature parameter we could train a model using any classification algorithms to come up with some set of boundary conditions which can be used to differentiate the male and female genders using hair length as the training feature. In gender classification case the boundary condition could the proper hair length value. Suppose the differentiated boundary hair length value is 15.0 cm then we can say that if hair length is less than 15.0 cm then gender could be male or else female.<\/p>\n<p>Some classification algorithms listed below.<\/p>\n<h2>Classification Algorithms<\/h2>\n<ul>\n<li>Linear classifiers\n<ul>\n<li>Logistic regression<\/li>\n<li>Naive Bayes classifier<\/li>\n<li>Fisher&#8217;s linear discriminant<\/li>\n<\/ul>\n<\/li>\n<li>Support vector machines\n<ul>\n<li>Least squares support vector machines<\/li>\n<\/ul>\n<\/li>\n<li>Quadratic classifiers<\/li>\n<li>Kernel estimation\n<ul>\n<li>k-nearest neighbor<\/li>\n<\/ul>\n<\/li>\n<li>Decision trees\n<ul>\n<li>Random forests<\/li>\n<\/ul>\n<\/li>\n<li>Neural networks<\/li>\n<li>Learning vector quantization<\/li>\n<\/ul>\n<h2>Application of Classification Algorithms<\/h2>\n<ul>\n<li>Email spam classification<\/li>\n<li>Bank customers\u00a0loan pay bank willingness\u00a0prediction.<\/li>\n<li>Cancer tumour cells identification.<\/li>\n<li>Sentiment analysis.<\/li>\n<li>Drugs classification<\/li>\n<li>Facial key points detection<\/li>\n<li>Pedestrians detection in an automotive car driving.<\/li>\n<\/ul>\n<h2>Clustering Concept<\/h2>\n<div id=\"attachment_889\" style=\"width: 606px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-889\" class=\"wp-image-889 lazyload\" data-src=\"http:\/\/dataaspirant.com\/wp-content\/uploads\/2016\/09\/Clustering.png\" alt=\"Clustering Algorithms\" width=\"596\" height=\"500\" data-srcset=\"https:\/\/dataaspirant.com\/wp-content\/uploads\/2016\/09\/Clustering.png 940w, https:\/\/dataaspirant.com\/wp-content\/uploads\/2016\/09\/Clustering-300x251.png 300w, https:\/\/dataaspirant.com\/wp-content\/uploads\/2016\/09\/Clustering-768x644.png 768w\" data-sizes=\"(max-width: 596px) 100vw, 596px\" src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" style=\"--smush-placeholder-width: 596px; --smush-placeholder-aspect-ratio: 596\/500;\" \/><noscript><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-889\" class=\"wp-image-889\" src=\"http:\/\/dataaspirant.com\/wp-content\/uploads\/2016\/09\/Clustering.png\" alt=\"Clustering Algorithms\" width=\"596\" height=\"500\" srcset=\"https:\/\/dataaspirant.com\/wp-content\/uploads\/2016\/09\/Clustering.png 940w, https:\/\/dataaspirant.com\/wp-content\/uploads\/2016\/09\/Clustering-300x251.png 300w, https:\/\/dataaspirant.com\/wp-content\/uploads\/2016\/09\/Clustering-768x644.png 768w\" sizes=\"(max-width: 596px) 100vw, 596px\" \/><\/noscript><p id=\"caption-attachment-889\" class=\"wp-caption-text\"><strong>Clustering<\/strong><\/p><\/div>\n<p>In clustering the idea is not to predict the target class as like classification , it&#8217;s more ever trying to group the similar kind of things by considering the most satisfied condition <strong>all the items in the same group should be similar and no two different group items should not be similar. \u00a0<\/strong>To group the similar kind of items in clustering, different <a href=\"http:\/\/dataaspirant.com\/2015\/04\/11\/five-most-popular-similarity-measures-implementation-in-python\/\" target=\"_blank\">similarity measures<\/a> could be used.<\/p>\n<h3>Group items Examples:<\/h3>\n<ul>\n<li>While grouping similar language type documents (Same language documents are one group.)<\/li>\n<li>While categorising the news articles (Same news category(Sport) articles are one group )<\/li>\n<\/ul>\n<p>Let&#8217;s understand the concept with clustering genders based on hair length example. To determine gender, different similarity measure could be used to categorise male and female genders. This could be done by finding the similarity between two hair lengths and keep them in the same group if the similarity is less(Difference of hair length is less). The same process could continue until all the hair length properly grouped into two categories.<\/p>\n<p>To get used to different similarity measure to\u00a0perform clustering we have some popular clustering algorithms. Which are listed below.<\/p>\n<h2>Clustering Algorithms<\/h2>\n<p>Clustering algorithms can be classified into two main categories Linear clustering algorithms and Non-linear clustering algorithms.<\/p>\n<ul>\n<li><strong>Linear clustering algorithm<\/strong>\n<ul>\n<li>k-means clustering algorithm<\/li>\n<li>Fuzzy c-means clustering algorithm<\/li>\n<li>Hierarchical clustering algorithm<\/li>\n<li>Gaussian(EM) clustering algorithm<\/li>\n<li>Quality threshold clustering algorithm<\/li>\n<\/ul>\n<\/li>\n<li><strong>Non-linear clustering algorithm<\/strong>\n<ul>\n<li>MST based clustering algorithm<\/li>\n<li>kernel k-means clustering algorithm<\/li>\n<li>Density-based clustering algorithm<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h2>Application of Clustering Algorithms<\/h2>\n<ul>\n<li>Recommender systems<\/li>\n<li>Anomaly detection<\/li>\n<li>Human genetic clustering<\/li>\n<li>Genom Sequence analysis<\/li>\n<li>Analysis of antimicrobial activity<\/li>\n<li>Grouping of shopping items<\/li>\n<li>Search result grouping<\/li>\n<li>Slippy map optimization<\/li>\n<li>Crime analysis<\/li>\n<li>Climatology<\/li>\n<\/ul>\n<h2>Conclusion<\/h2>\n<p>Let&#8217;s summarize the key things we have learnt in this blog post.<\/p>\n<p><strong>Classification:<\/strong>\u00a0Predicting target class for test dataset from the trained modeled from the training dataset.<\/p>\n<p><strong>Clustering:<\/strong> Using different similarity measure to place the all the similar items in a group.<\/p>\n<h2>References<\/h2>\n<p>[1]\u00a0<a href=\"https:\/\/en.wikipedia.org\/wiki\/Statistical_classification\" target=\"_blank\">Statistical classification<\/a><\/p>\n<p>[2] <a href=\"https:\/\/sites.google.com\/site\/dataclusteringalgorithms\/home\" target=\"_blank\">Data Clustering<\/a><\/p>\n<p>[3] <a href=\"http:\/\/scikit-learn.org\/stable\/modules\/clustering.html\" target=\"_blank\">Scikit-learn Clustering<\/a><\/p>\n<h4>Follow us:<\/h4>\n<h5><strong><span style=\"color: #0000ff;\"><a style=\"color: #0000ff;\" title=\"dataaspirant\" href=\"https:\/\/www.facebook.com\/dataaspirant\" target=\"_blank\">FACEBOOK<\/a><\/span>|\u00a0<span style=\"color: #ff0000;\"><a style=\"color: #ff0000;\" title=\"dataaspirant.quora.com\" href=\"http:\/\/dataaspirant.quora.com\/\" target=\"_blank\">QUORA<\/a><\/span>\u00a0|<span style=\"color: #00ccff;\"><a style=\"color: #00ccff;\" title=\"twitter.com\/dataaspirant\" href=\"https:\/\/twitter.com\/dataaspirant\" target=\"_blank\">TWITTER<\/a><\/span>| <span style=\"color: #808000;\"><a style=\"color: #808000;\" href=\"https:\/\/plus.google.com\/104087213513583475222\" target=\"_blank\">GOOGLE+<\/a><\/span> | <span style=\"color: #000080;\"><a style=\"color: #000080;\" href=\"https:\/\/www.linkedin.com\/company\/dataaspirant\" target=\"_blank\">LINKEDIN<\/a><\/span>|\u00a0<span style=\"color: #ff9900;\"><a style=\"color: #ff9900;\" title=\"reddit dataaspirant\" href=\"http:\/\/www.reddit.com\/user\/dataaspirant\/\" target=\"_blank\">REDDIT<\/a>\u00a0<\/span>| <span style=\"color: #ff0000;\"><a style=\"color: #ff0000;\" title=\"flipboard dataaspirant\" href=\"https:\/\/flipboard.com\/@dataaspirant\/\" target=\"_blank\">FLIPBOARD<\/a><\/span> | <span style=\"color: #00ff00;\"><a style=\"color: #00ff00;\" title=\"medium dataaspirant\" href=\"https:\/\/medium.com\/@dataaspirant\" target=\"_blank\">MEDIUM<\/a>\u00a0| <a href=\"https:\/\/github.com\/saimadhu-polamuri\/DataAspirant_codes\" target=\"_blank\"><span style=\"color: #008080;\">GITHUB<\/span><\/a><\/span><\/strong><\/h5>\n<p>I hope you like this post. If you have any questions then feel free to comment below.\u00a0 If you want me to write on one specific topic then do tell it to me in the comments below.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Solving real world problems with data science concepts is so exciting and it yields so fun. A famous dialogue you could listen from the\u00a0data science people. It could be true if we add it&#8217;s so challenging\u00a0at the end of the dialogue. The foremost challenge starts from \u00a0categorising the problem itself. The first level of categorising<br \/><a class=\"moretag\" href=\"https:\/\/dataaspirant.com\/classification-clustering-alogrithms\/\">+ Read More<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":"","_jetpack_memberships_contains_paid_content":false},"categories":[3,4,6],"tags":[39,40],"aioseo_notices":[],"jetpack_sharing_enabled":true,"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/dataaspirant.com\/wp-json\/wp\/v2\/posts\/875"}],"collection":[{"href":"https:\/\/dataaspirant.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dataaspirant.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dataaspirant.com\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/dataaspirant.com\/wp-json\/wp\/v2\/comments?post=875"}],"version-history":[{"count":0,"href":"https:\/\/dataaspirant.com\/wp-json\/wp\/v2\/posts\/875\/revisions"}],"wp:attachment":[{"href":"https:\/\/dataaspirant.com\/wp-json\/wp\/v2\/media?parent=875"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dataaspirant.com\/wp-json\/wp\/v2\/categories?post=875"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dataaspirant.com\/wp-json\/wp\/v2\/tags?post=875"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}