Overlapping-Based Rough Clustering Algorithm
基于覆盖的粗糙聚类算法

Most of traditional clustering algorithms get a partition of sample set with mutually exclusive classes, while there is no explicit boundary between classes mostly in the real world. Introducing rough set theory into clustering analysis, this paper proposes a kind of overlapping-based rough clustering algorithm called KMMRSC which represents a class with multiple centroids and describes the belongingness of samples with the concepts of upper approximation and lower approximation, thus there is overlapping relationship between classes. Experiments show that the algorithm KMMRSC, which can find non-spherical clusters, outperforms classic k-means.