Balanced K-Means for Clustering

We present a k-means-based clustering algorithm, which optimizes mean square error, for given cluster sizes. A straightforward application is balanced clustering, where the sizes of each cluster are equal. In k-means assignment phase, the algorithm solves the assignment problem by Hungarian algorithm. This is a novel approach, and makes the assignment phase time complexity O(n), which is faster than the previous O(kn) time linear programming used in constrained k-means. This enables clustering of bigger datasets of size over 5000 points.