Overlap regulation for additive overlapping clustering methods

Abstract

Overlapping Clustering is an important technique in machine learning which aims to organize data into a set of non-disjoint groups rather than the disjoint one which is the case of conventional clustering methods. Several machine learning applications require that data object be assigned to one or several groups resulting in non-disjoint partitioning of data such as document clustering where each document can discuss one or many topics and then must be assigned to one or several groups. This paper presents a new partitional overlapping clustering method based on the additive model of overlaps. Compared to existing methods which build clusters with fixed size of overlaps, the proposed method gives users the ability to regulate this size. Experiments performed on simulated and real datasets show the performance of the proposed regulation principle to control the size of overlaps among groups.