Research and Progress of Cluster Algorithms based on Granular Computing

Abstract

Granular Computing (GrC), a knowledge-oriented computing which covers the theory of fuzzy information granularity, rough set theory, the theory of quotient space and interval computing etc, is a way of dealing with incomplete, unreliable, uncertain fuzzy knowledge. In recent years, it is becoming one of the main study streams in Artificial Intelligence (AI). With selecting the size structure flexibly, eliminating the incompatibility between clustering results and priori knowledge, completing the clustering task effectively, cluster analysis based on GrC attracts great interest from domestic and foreign scholars. In this paper, starting from the development of GrC, firstly, the main newly achievements about clustering and GrC are researched and summarized. Secondly, principle of granularity in clustering, the effective clustering algorithms with the idea of granularity as well as their merits and faults are analyzed and evaluated from the point view of rough set, fuzzy sets and quotient space theories. Finally, the feasibility and effectiveness of handling high-dimensional complex massive data with combination of these theories is outlooked.