Can someone explain to me how they got to the answer here? Was euclidean distance used on each point and then the closest points were clustered? Thank you

K-Means Clustering Example 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),Cl(1, 2),C2(4, 9) The distance function is Euclidean distance. Suppose initially we assign A1, B1, and Cl 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 (b) the final three clusters. 13 K-Means Clustering Example (a) the three cluster centers after the first round of execution Answer: After the first round, the three new clusters are: (1) fA1 (2) 1B1,A3,B2,B3,C2 (3) sC1,A2, and their centers are (1) (2, 10), (2) (6, 6), (3) (1.5, 3.5) (b) the final three clusters. Answer: The final three clusters are: (1) sA1,C2,B1, (2) sA3,B2,BB 14