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K-Means                                                  Clustering



                          Randomly Select                  Each Object Assigned              Clusters Centres Updated
                          K-Clusters (K=2)                  To Similar Centroid           Depending On Renewed Cluster
                                                                                                    Mean

                  100                                100                                 100
                  90                                 90                                   90
                  80                                 80                                   80
                  70                                 70                                   70
                  60                                 60                                   60
                  50                                 50                                   50
                  40                                 40                                   40
                  30                                 30                                   30
                  20                                 20                                   20
                  10                                 10                                   10
                   0                                  0                                   0
                      0  10  20  30  40  50  60  70  80  90  100  0  10  20  30  40  50  60  70  80  90  100  0  10  20  30  40  50  60  70  80  90  100

                                          Re-Assign                                               Re-Assign
                                         Data Points                                             Data Points
                                                                        Update Cluster
                                          100                                    Centres 100
                                           90                                           90
                                           80                                           80
                                           70                                           70
                                           60                                           60
                                           50                                           50
                                           40                                           40
                                           30                                           30
                                           20                                           20
                                           10                                   Iterative  10
                                            0                                           0
                                              0  10  20  30  40  50  60  70  80  90  100  Process  0  10  20  30  40  50  60  70  80  90  100


                                                                                                      Experiential Learning
                          Video Session


                     Scan the QR code or visit the following link to watch the video: StatQuest: K-means Clustering
                     https://www.youtube.com/watch?v=4b5d3muPQmA
                     After watching the video, answer the following question:

                     What do you mean by K-means clustering according to the video?









              Advantages of K-Means
              Some of the advantages of K-Means Clustering are:
              •  Quite simple to implement.

              •  Can handle large data sets.
              •  Can give initial positions to centroids (randomly).
              •  Easily adapts to new data.
              •  Can easily adapt to clusters of different shapes and sizes, like elliptical clusters.





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