Abstract: Information granularity is a measure of different levels for refining information and knowledge. With the advantages of selecting granularity structure flexibly, eliminating incompatibility between clustering results and priori knowledge, completing clustering task effectively, granularity clustering methods become one of the focus at home and abroad. In this paper, combined the traditional clustering algorithms from the view of rough set, fuzzy set and ctuotient space theories, effective clustering algorithms with the idea of granularity and their merits and faults were studied and generalized. Finally, the feasibility and effectiveness of handling high-dimensional complex massive data with combina- lion of these theories were forecasted and outlooked.

Key words: Information granularity, Rough set, Fuzzy set, Theory of quotient space, Clustering algorithm