Suppose that the data mining task is to cluster points (with (x, y) representing location) into three clusters, where the points are:

A1(2, 10), A2(2, 5), A3(8, 4), B1(5, 8), B2(7, 5), B3(6, 4), C1(1, 2), C2(4, 9)

The distance function is Euclidean distance. Suppose initially we assign A1, B1, and C1 as the center of each cluster, respectively. Use the k-means algorithm to show:((Show the steps ))

(a) The three cluster centers after the first round of execution.

(b) The final three clusters.

7. Suppose that the data mining task is to cluster the following eight points (with (x, y) representing location) into three clusters. A1 (2, 10), A2 (2, 5), (8, 4), B1 (5, 8), B2 (7, 5), B3 (6, 4), Cl (1, 2), C2 (4,9). The distance function is Euclidean distance. Suppose initially we assign A1,B1 and C1 as the center of each cluster, respectively. Use the k-means algorithm to show only (a) The three cluster centers after the first round of execution and (b) The final three clusters