Machine Learning

                                                                                            UNIT 5 (B)                                                                       Clustering 5.      How does clustering work 6.      finding similarities using distances 7.      Euclidean distance and other distance metrics k-Means Clustering: 8.      Plotting customers with their segments 9.      normalizing features 10.   cluster centres and interpreting the Clusters 11.   Hierarchical Clustering   UNIT-5 (B) Long Answer Questions 1.      5.      Explain Hierarchical Clustering algorithm with example. 6.      What is the purpose of normalizing features?  How it can perform? Explain. 7.      How can you find the similarities using distances? List the different distance metrics. 8.      How can you be plotting the customers with their segments? Explain.   Short Answer Questions 1.   4.      What is the purpose of cluster centre? 5.      How can you d

   Unit 5 B Clustering How does clustering work finding similarities using distances Euclidean distance and other distance metrics k-Means Clustering: Plotting customers with their segments normalizing features cluster centres and interpreting the Clusters   Hierarchical Clustering ------------------------------------------------------------------------------------------------------------------------

  Unit 5 B Clustering 5.      How does clustering work 6.      finding similarities using distances 7.      Euclidean distance and other distance metrics k-Means Clustering: 8.      Plotting customers with their segments 9.      normalizing features 10.   cluster centres and interpreting the Clusters 11.   Hierarchical Clustering -------------------------------------------------------------------------------------------------------------------------