Final answer:
The k-means algorithm indeed minimizes the overall coherence by finding the partition of data that has the least within-cluster variance, where the centroids adjust until reaching minimal sum of squared distances from points to centroids.
Step-by-step explanation:
True. The k-means algorithm converges by adjusting the centroids of clusters to minimize the overall within-cluster variance, also known as coherence. This iterative process continues until the sum of the squared distances between each point and the centroid of the cluster to which it belongs is at its minimum. This means that the objective of the k-means algorithm is to find a partition of the data that minimizes the sum of the squared differences between the data points and the respective cluster centroids, effectively minimizing total within-cluster variation or coherence.