A Three-Way Decisions Approach to “”Density-Based Overlapping Clustering
Most of clustering methods assume that each object must be assigned to exactly one cluster, however, overlapping clustering is more appropriate than crisp clustering in a variety of important applications such as the network structure analysis and biological information. This paper provides a three-way decisions approach for overlapping clustering based on the decision-theoretic rough set model, where each cluster is described by an interval set which is defined by a pair of sets called the lower and upper bounds, and the overlapping objects usually are distributed in the region between the lower and upper regions. Besides, a density-based clustering algorithm is proposed using the approach considering the advantages of the density-based clustering algorithms in finding the arbitrary shape clusters. The results of comparison experiments show that the three-way decisions approach is not only effective to overlapping clustering but also good at discovering the arbitrary shape clusters.
KeywordsOverlapping clustering Three-way decisions Decision-theoretic rough set theory Density-based clustering Data mining
This work was supported in part by the China NSFC grant (No.61379114 & No.61272060).
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