The endpoint of a k-means clustering algorithm occurs whenGroup of answer choices

The endpoint of a k-means clustering algorithm occurs when the following conditions are met:

1. Centroids Stabilization: The centroids of the clusters do not change significantly between iterations. This means the algorithm has converged to a solution where the center of each cluster represents the average of the data points assigned to it.

2. No Change in Cluster Assignment: The assignment of data points to clusters remains the same from one iteration to the next. This indicates that all points are optimally assigned to their respective clusters based on the current centroids.

3. Maximum Iterations Reached: The algorithm has completed a pre-defined number of iterations, regardless of whether centroids have stabilized.

In practice, the k-means algorithm continues to iterate through these steps until one of these conditions is satisfied, particularly focusing on minimizing the sum of squared distances (inertia) between points and their respective cluster centroids. If you're looking at possible multiple-choice answers, they would typically revolve around these concepts.