Fixed-sized clusters k-Means

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.

Tasks

Reproductions