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Global $k$-means$++$: an effective relaxation of the global $k$-means clustering algorithm. (arXiv:2211.12271v1 [cs.LG])
Nov. 23, 2022, 2:12 a.m. | Georgios Vardakas, Aristidis Likas
cs.LG updates on arXiv.org arxiv.org
The $k$-means algorithm is a very prevalent clustering method because of its
simplicity, effectiveness, and speed, but its main disadvantage is its high
sensitivity to the initial positions of the cluster centers. The global
$k$-means is a deterministic algorithm proposed to tackle the random
initialization problem of k-means but requires high computational cost. It
partitions the data to $K$ clusters by solving all $k$-means sub-problems
incrementally for $k=1,\ldots, K$. For each $k$ cluster problem, the method
executes the $k$-means algorithm …
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