Question

In K-means clustering, you assign each data point to

A. the cluster that has its centroid located closest to the point.
B. the cluster that has its centroid located furthest from the point.
C. a randomly selected cluster.
D. a location that is not part of a cluster.

Answer 1

In K-means clustering, data points are assigned to the cluster with the nearest centroid, with the aim of minimizing intra-cluster variance.

Step-by-step explanation:

In K-means clustering, each data point is assigned to the cluster that has its centroid located closest to the point. This is the fundamental mechanism by which K-means clustering assigns data points to clusters, essentially determining which cluster each data point is most similar to, based on distance to the cluster's center. The initial centroids are chosen randomly, and then the K-means algorithm iteratively refines the positions of the centroids by minimizing the variance within each cluster. The purpose of K-means clustering is to partition the dataset into clusters that minimize the intra-cluster variance, hence assigning data points to the nearest centroid.