{"id":70,"date":"2018-06-07T20:23:00","date_gmt":"2018-06-07T18:23:00","guid":{"rendered":"https:\/\/kindsonthegenius.com\/blog\/2018\/06\/07\/advanced-statistics-quiz-11-cluster-analysis\/"},"modified":"2020-08-22T10:17:31","modified_gmt":"2020-08-22T08:17:31","slug":"advanced-statistics-quiz-11-cluster-analysis","status":"publish","type":"post","link":"https:\/\/kindsonthegenius.com\/blog\/advanced-statistics-quiz-11-cluster-analysis\/","title":{"rendered":"Advanced Statistics Quiz 11 &#8211; Cluster Analysis"},"content":{"rendered":"<p>Good to see you here!<br \/>\nToday&#8217;s quiz would be based on Cluster Analysis. So let&#8217;s get started.<\/p>\n<div style=\"clear: both; text-align: center;\"><\/div>\n<p><b>Question 1: What is Cluster Analysis?<\/b><br \/>\nCluster Analysis is the statistical procedure that is aimed at grouping data object based on the information found in the data set that describes the objects and their attributes<\/p>\n<p>&nbsp;<\/p>\n<p><b>Question 2: What is the Goal of Cluster Analysis?<\/b><br \/>\nThe objective of cluster analysis is to group objects with similar characteristics into one cluster.<\/p>\n<p>&nbsp;<\/p>\n<p><b>Question 3: What are the two types of Clustering?<\/b><br \/>\nThe two types of clustering are:<br \/>\nHierarchical Clustering: Clusters are arranged in a hierarchical tree<br \/>\nPartitioning Clustering: Data are grouped into distinct subsets that does not overlap<\/p>\n<p>&nbsp;<\/p>\n<p><b>Question 4: Describe the k-Means Clustering<\/b><br \/>\nK-Means clustering is a partitioning clustering approach where each cluster is\u00a0 associated with a centroid or center point and each data point is assigned to\u00a0 the centroid that is closest to it. The number of clusters is specified in advance.<\/p>\n<p>&nbsp;<\/p>\n<p><b>Question 5: Write the k-Means Clustering Algorithm?<\/b><br \/>\ni. Choose the initial value of K<br \/>\nii. <b>repeat<\/b><br \/>\niii. Form K clusters by assigning each point to the closest centroid<br \/>\niv. Recalculate the centroid of each cluster<br \/>\nv. Move the centroid to the new computed position<br \/>\nvi. <b>until <\/b>The centroids position don&#8217;t change<\/p>\n<p>&nbsp;<\/p>\n<p><b>Question 6: How do you Choose Initial Value of K for k-Means Clustering<\/b><\/p>\n<ul>\n<li>Use another clustering method to estimate it<\/li>\n<li>Run the algorithm with different values of K and then choose the one that is optimal<\/li>\n<li>Use the prior knowledge about the characteristics of the data<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><b>Question 7: How do you choose the centroid for the cluster?<\/b><\/p>\n<ul>\n<li>Random selection from the feature space<\/li>\n<li>Random selection from the data set<\/li>\n<li>Look for dense regions of space<\/li>\n<li>Space them uniformly around the feature space<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><b>Question 8:\u00a0 How is the quality of a cluster measured?<\/b><\/p>\n<ul>\n<li>The size of the cluster vs the distance between the clusters<\/li>\n<li>The Distance between members of the clusters<\/li>\n<li>The Diameter of the smallest sphere<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><b>Question 9: What are some limitations of k-Means Clustering?<\/b><br \/>\nNot efficient if data contains outliers<br \/>\nFails for non-convex round clusters<\/p>\n<p>&nbsp;<\/p>\n<p><b>Question 9: What is McQueen&#8217;s Algorithm used for?<\/b><br \/>\nThe McQueen&#8217;s Algorithm is used for measuring the goodness of the clustering and for minimizing the compactness function in finite steps<\/p>\n<p>&nbsp;<\/p>\n<p><b>Question 10: Outline and explain the two types of Hierarchical Clustering<\/b><br \/>\nThe two types of hierarchical clustering are:<br \/>\nTop-Down Clustering<br \/>\nBottom-Top Clustering<\/p>\n<p><i>How Bottom-Top or Agglomerative Clustering work<\/i><\/p>\n<ul>\n<li>Start with each of the data points in its own cluster<\/li>\n<li>Merge two clusters that are similar<\/li>\n<li>Repeat the merging until there is a single cluster of all he data points<\/li>\n<\/ul>\n<p><i>How Top-Down or Divisive Clustering Work<\/i><\/p>\n<ul>\n<li>Start with all examples in one big cluster<\/li>\n<li>Remove the data point that seems to far away from other points<\/li>\n<li>Repeat the process until all points is in its own cluster<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><b>Question 11: Mention three ways to compute dissimilarity between clusters<\/b><\/p>\n<ul>\n<li>Single Link<\/li>\n<li>Complete Link<\/li>\n<li>Group Average<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><b>Question 12: Compare k-Means and Hierarchical Clustering<\/b><br \/>\nk-Means produces single partition while hierarchical produces different partitions<br \/>\nk-Means needs the number of clusters specified in advance while hierarchical does not<br \/>\nk-Means is have a more efficient run-time than the hierarchical<\/p>\n<p>&nbsp;<\/p>\n<p><b>Question 13: What is a Dendrogram?<\/b><br \/>\nA dendrogram is a tree diagram used to illustrate the arrangement of clusters in hierarchical clustering.<\/p>\n<p>&nbsp;<\/p>\n<p>I would stop here so I can allow you some time to get your head around these concepts.<br \/>\nThank you for reading.!<br \/>\nFeel free to check out the quiz on other Statistics topics.<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Good to see you here! Today&#8217;s quiz would be based on Cluster Analysis. So let&#8217;s get started. Question 1: What is Cluster Analysis? Cluster Analysis &hellip; <\/p>\n","protected":false},"author":2,"featured_media":597,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_mi_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0},"categories":[15,552],"tags":[],"_links":{"self":[{"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/posts\/70"}],"collection":[{"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/comments?post=70"}],"version-history":[{"count":3,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/posts\/70\/revisions"}],"predecessor-version":[{"id":598,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/posts\/70\/revisions\/598"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/media\/597"}],"wp:attachment":[{"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/media?parent=70"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/categories?post=70"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/tags?post=70"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}