This unsupervised clustering algorithm terminates when mean values computed for the current iteration of the algorithm are identical to the computed mean values for the previous iteration.
A) agglomerative clustering
B) conceptual clustering
C) K-Means clustering
D) expectation maximization

Question

Quizplus · Accepted Answer

The described termination condition is specific to the K-Means clustering algorithm, which iteratively assigns data points to the nearest cluster center, updates the mean values for each cluster, and repeats until convergence. Once the means for the current iteration are identical to the means for the previous iteration, further iterations will not change the cluster assignments, so the algorithm terminates. Agglomerative clustering and conceptual clustering do not use mean values in this way, and EM does not necessarily converge when mean values are identical between iterations.

This Unsupervised Clustering Algorithm Terminates When Mean Values Computed for the Current