Question

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 only

Answer 1

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 centre of each cluster, respectively. Use the k-means algorithm to show only

Solution:

a) After the first round the three clysters would be  

(1) {A1}  (2) {B1, A3, B2, B3, C2} (3) {C1, A2}

And their respective centres 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}.