3. (15 points) Use the K-Means algorithm and Euclidean distance to cluster the following 8 data points into 3 clusters:

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3 15 Points Use The K Means Algorithm And Euclidean Distance To Cluster The Following 8 Data Points Into 3 Clusters 1 (91.69 KiB) Viewed 45 times

3. (15 points) Use the K-Means algorithm and Euclidean distance to cluster the following 8 data points into 3 clusters: A1 = (2, 10), A2 = (2,5), A3 = (8,4), A4 = (5,8), A5 = (7,5), A6 = (6,4), A7 = (1, 2), A8 = (4,9). Suppose the initial centroids are A1, A4 and A7. The following figure shows the data points. Run the K-Means algorithm until converge. Show the intermediate results (including the objective value, the cluster assignments, and the centroids) of all K-Means iterations, where an iteration consists of re-assigning clusters to data points and then re- calculating centroids. A A1 10 10 A8 9 9 A8 A4 8 A 04 8 7 7 6 6 A2 A5 A5 5 5 5 А 2 A6 A3 A6 A3 4 4 3 3 A7 A7 N 2 1 1 0 0 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10