The objective function in k-means clustering attempts to
A) minimize the sum of within-cluster dispersions for all clusters
B) minimize the sum of between-cluster dispersions for all clusters
C) maximize the sum of within-cluster dispersions for all clusters
D) maximize the sum of between-cluster dispersions for all clusters

Question

Quizplus · Accepted Answer

The objective function in k-means clustering is to minimize the sum of within-cluster dispersions for all clusters. This means that the algorithm tries to assign each point to the cluster whose centroid is closest to that point, in order to minimize the sum of distances between points and their assigned centroid. The within-cluster dispersion is a measure of how spread out the points are within each cluster, so minimizing it ensures that the clusters are compact and well-separated.

The Objective Function in K-Means Clustering Attempts to