Qualitative Properties of the Minimum Sum-of-Squares Clustering Problem

TL;DR

This paper explores fundamental qualitative properties of the minimum sum-of-squares clustering problem, including solution existence, solution characteristics, and stability properties, providing insights into the behavior of clustering algorithms.

Contribution

It establishes new theoretical results on solution existence, properties of global and local solutions, and stability measures, with explicit formulas and necessary and sufficient conditions.

Findings

Global solutions always exist

Global solution set is finite under mild conditions

Optimal value function is locally Lipschitz

Abstract

A series of basic qualitative properties of the minimum sum-of-squares clustering problem are established in this paper. Among other things, we clarify the solution existence, properties of the global solutions, characteristic properties of the local solutions, locally Lipschitz property of the optimal value function, locally upper Lipschitz property of the global solution map, and the Aubin property of the local solution map. We prove that the problem in question always has a global solution and, under a mild condition, the global solution set is finite and the components of each global solution can be computed by an explicit formula. Based on a newly introduced concept of nontrivial local solution, we get necessary conditions for a system of centroids to be a nontrivial local solution. Interestingly, we are able to show that these necessary conditions are also sufficient ones. Finally, it is proved that the optimal value function is locally Lipschitz, the global solution map is locally upper Lipschitz, and the local solution map has the Aubin property, provided that the original data points are pairwise distinct. Thanks to the obtained complete characterizations of the nontrivial local solutions, one can understand better the performance of not only the $k$-means algorithm, but also of other solution methods for the minimum sum-of-squares clustering problem.