Big-means: Less is More for K-means Clustering. (arXiv:2204.07485v1 [cs.LG])

K-means clustering plays a vital role in data mining. However, its
performance drastically drops when applied to huge amounts of data. We propose
a new heuristic that is built on the basis of regular K-means for faster and
more accurate big data clustering using the "less is more" and MSSC
decomposition approaches. The main advantage of the proposed algorithm is that
it naturally turns the K-means local search into global one through the process
of decomposition of the MSSC problem. …

arxiv big clustering k-means