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Clustering by the Probability Distributions from Extreme Value Theory
[article]
2022
arXiv
pre-print
Clustering is an essential task to unsupervised learning. It tries to automatically separate instances into coherent subsets. As one of the most well-known clustering algorithms, k-means assigns sample points at the boundary to a unique cluster, while it does not utilize the information of sample distribution or density. Comparably, it would potentially be more beneficial to consider the probability of each sample in a possible cluster. To this end, this paper generalizes k-means to model the
arXiv:2202.09784v1
fatcat:vf7e35hwqfbw3efmzzxt5hnvmi