Suppose that the data mining task is to cluster points with

Suppose that the data mining task is to cluster points (with (x, y) representing location) into three clusters, where the points are -A_1 (2, 10), A_2 (2, 5), A_3 (8, 4), B_1 (5, 8), B_2 (7, 5), B_3 (6, 4), C_1(1, 2), C_2 (4, 9). The distance function is Euclidean distance. Suppose initially we assign A_1, B_1, and C_1 as the center of each cluster, respectively. Use the k-means algorithm to show only The three cluster centers after the first round of execution. The final three clusters.

Solution

Solution:

(a) After the first round, the three new clusters are: (1) {A1 }, (2) {B1 , A3 , B2 , B3 , C2 }, (3) {C1 , A2 }, and their centers are (1) (2, 10), (2) (6, 6), (3) (1.5, 3.5).

(b) The final three clusters are: (1) {A1 , C2 , B1 }, (2) {A3 , B2 , B3 }, (3) {C1 , A2 }.