Variance based three-way clustering approaches for handling overlapping clustering

The conventional clustering approaches are not very effective in dealing with clusters having overlapping regions. The three-way clustering (3WC) is an effective and promising approach in this regards. A key issue in 3WC is the determination of thresholds which plays a crucial and important role in accurate estimation of the overlapping region. In this article, we propose different variance based criteria for determining the thresholds. In particular, we examine the variance or spread in evaluation function values of objects contained in the three regions obtained with 3WC of objects. An algorithm called 3WC-OR is introduced that considers the optimization of the proposed criteria for determining effective thresholds by incorporating approaches such as genetic algorithms and game-theoretic rough sets. Experimental results on five UCI datasets indicate that the proposed algorithm significantly improves results on datasets with overlapping clusters and provide comparable results on datasets with non-overlapping clusters. (C) 2019 Elsevier Inc. All rights reserved.