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 Ci as the center of each cluster, respectively. The final three cluster centers using the k-means algorithm are: Select one: a. (1) (A1, C2, B1), (2) {A3, B3), (3) {B2, C1, A2) b. (1) (A1), (2) {B1, A3, B2, B3, C2), (3) {C1, A2} c. (1) {A1 ,B1), (2) {A3, B2, B3), (3) {C1, C2, A2} d. (1) (A1, C2, B1), (2) {A3, B2, B3), (3) {C1, A2)