A Three-Way Clustering Mechanism to Handle Overlapping Regions

The conventional clustering methods assume a binary classification and establish a complete inclusive or exclusive type relation of an object with a cluster. In contrast, a three-way paradigm handles situations where an object may or may not belong to a cluster, i.e., uncertain. The objects belonging to the uncertainty region may lead to inclusion or exclusion after further processing and information. One of the use cases of the three-way paradigm is the overlapping region between different clusters. Effective computation of overlapping objects is crucial to the application's overall success. In this paper, we employ a three-way clustering approach inspired by image blurring and sharpening operations that consider the objects in the inside or outside regions of a cluster to be non-overlapping. The objects belonging to the partial region of more than one cluster are considered overlapping. The experiment conducted on Birds, Scenes, and 20 newsgroups datasets indicates that the proposed approach improves the F1 measure and hamming loss up to 18.6% and 4.9%, respectively. Furthermore, the system's robustness for overlapping regions is observed using typical clustering measures. The experimental results suggested that the proposed approach may improve the computation of overlapping regions effectively.