Data clustering with size constraints

Abstract

Data clustering is an important and frequently used unsupervised learning method. Recent research has demonstrated that incorporating instance-level background information to traditional clustering algorithms can increase the clustering performance. In this paper, we extend traditional clustering by introducing additional prior knowledge such as the size of each cluster. We propose a heuristic algorithm to transform size constrained clustering problems into integer linear programming problems. Experiments on both synthetic and UCI datasets demonstrate that our proposed approach can utilize cluster size constraints and lead to the improvement of clustering accuracy.