Fixed-sized clusters k-Means

Mikko I. Malinen, Pasi Fränti
1/27/2025
cs.LG

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Abstract

We present a $k$-means-based clustering algorithm, which optimizes the mean square error, for given cluster sizes. A straightforward application is balanced clustering, where the sizes of each cluster are equal. In the $k$-means assignment phase, the algorithm solves an assignment problem using the Hungarian algorithm. This makes the assignment phase time complexity $O(n^3)$. This enables clustering of datasets of size more than 5000 points.

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Cite this paper

@article{malinen2025fixedsized,
  title  = {Fixed-sized clusters k-Means},
  author = {Mikko I. Malinen and Pasi Fränti},
  year   = {2025},
  eprint = {2501.16113},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2501.16113v1}
}

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