Comment on Toward Increased k-means Clustering Efficiency with the Naive Sharding Centroid Initialization Method by https://thoitiet.online/vinh-long/

The k-means algorithm is a simple yet effective approach to clustering. k points are (usually) randomly chosen as cluster centers, or centroids, and all dataset instances are plotted and added to the closest cluster. After all instances have been added to clusters, the centroids, representing the mean of the instances of each cluster are re-calculated, with these re-calculated centroids becoming the new centers of their respective clusters. Cluster membership is then reset, and the new centroids are used to re-plot and all instances and add them to their closest centroid cluster. This iterative process continues until convergence -- centroid locations remain constant between successive clustering iterations -- is reached.

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