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arXiv · 2501.16113

Fixed-sized clusters $k$-Means

Mikko I. Malinen·Pasi Fränti
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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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Mikko I. Malinen, Pasi Fränti. 2025-01-27. Fixed-sized clusters $k$-Means. https://arxiv.org/abs/2501.16113

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