Categorizing Overlapping Regions in Clustering Analysis Using Three-Way Decisions

Clustering is a common technique for data analysis, has been widely used in many practical area. In many real applications such as social network analysis, wireless sensor networks, document clustering, and so on, there exist overlaps between different clusters due to various reasons. In this paper, we propose to use the three-way decisions approach to address categorizing overlapping regions. In contrast to existing soft clustering methods that just point out the objects whether in overlapping regions, the three-way decisions method provides a greater refinement of the categorization to system operators for further analysis, which is believed to show clearly the objects have different impacts to construct clusters. Besides, we provide a new relation-graph based clustering algorithm to obtain different overlapping region types. The results of comparison experiments are better and more reasonable to overlapping clustering.