Global optimization for cardinality-constrained minimum sum-of-squares clustering via semidefinite programming

Abstract The minimum sum-of-squares clustering (MSSC), or k-means type clustering, has been recently extended to exploit prior knowledge on the cardinality of each cluster. Such knowledge is used to increase performance as well as solution quality. In this paper, we propose a global optimization app