Video Summary

The paper studies the problem of finding the minimum number of clusters in a dataset such that the sum of distances between each point and the center of its cluster is minimized. The authors propose a new algorithm based on the k-means++ algorithm, which starts by choosing the initial centers randomly and then iteratively updates them until convergence. The algorithm is shown to be efficient and to produce good results on a variety of datasets.