the context of "MarketInsights" using k$-$means clustering for market segmentation, whic sexie ent most accurately describes the proces alerine ver anare er sormentation timeA. The algorithm maximizes the sum of the squared distances between data points and I corresponding cluster centroid, ensuring each cluster is as distinct as possible.B$.$ Each data point is assigned to the cluster with the farthest mean $($cluster centroid$)$ t clear differentiation between clusters.C$.$ The algorithm partitions the data into k clusters by minimizing the sum of the sq distances between data points and their corresponding cluster centroid, ensuring ea homogeneous as possible.D$.$ The objective of the k$-$means algorithm is to maximize the variance within ea highlight the differences between data points in the same cluster.E$.$ The algorithm aims to partition n observations into clusters where the sum o between data points within each cluster is maximized, creating distinct and ser