Multi-granulation Three-Way Clustering Ensemble Based on Shadowed Sets

The purpose of clustering ensemble is to find a unified partition of objects by fusing a set of clustering results. This paper proposes a multi-granulation three-way clustering ensemble algorithm based on shadowed sets to deal with the fuzzy and uncertainty data in the actual world. First, the algorithm generates a set of clustering members using the fuzzy c-means algorithm, and then the membership degree is mapped into three regions to construct three-way clustering. Second, the multi-granulation rough sets are used to construct four different approximate regions. Each cluster contains a core region and three boundary regions. Finally, the shadowed set is used to classify objects in boundary regions sequentially. Objects that cannot be divided are left in the boundary region. The experimental results show the algorithm obtains better clustering ensemble results in accuracy, adjust rand index, and normalized mutual information compared to multiple existing clustering ensemble algorithms. © 2021, Chinese Institute of Electronics. All right reserved.