Three-Way Spectral Clustering

In recent years, three-way clustering has shown promising performance in many different fields. In this paper, we present a new three-way spectral clustering by combining three-way decision and spectral clustering. In the proposed algorithm, we revise the process of spectral clustering and obtain an upper bound of each cluster. Perturbation analysis is applied to separate the core region from upper bound and the differences between upper bound and core region are regarded as the fringe region of specific cluster. The results on UCI data sets show that such strategy is effective in reducing the value of DBI and increasing the values of ACC and AS.