A novel cluster validity criterion for fuzzy c-regression model clustering algorithm

This paper proposes a novel cluster validity criterion for the fuzzy c-regression model (FCRM) clustering algorithm. The goal of the proposed cluster validity criterion is to decide the appropriate number of clusters in a FCRM. The simulation results demonstrate its validness and effectiveness.

[1]  J. Bezdek Cluster Validity with Fuzzy Sets , 1973 .

[2]  James C. Bezdek,et al.  On cluster validity for the fuzzy c-means model , 1995, IEEE Trans. Fuzzy Syst..

[3]  Euntai Kim,et al.  A new approach to fuzzy modeling , 1997, IEEE Trans. Fuzzy Syst..

[4]  Thomas A. Runkler,et al.  Some issues in system identification using clustering , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[5]  Donald Gustafson,et al.  Fuzzy clustering with a fuzzy covariance matrix , 1978, 1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes.

[6]  R.J. Hathaway,et al.  Switching regression models and fuzzy clustering , 1993, IEEE Trans. Fuzzy Syst..

[7]  Gerardo Beni,et al.  A Validity Measure for Fuzzy Clustering , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Graham C. Goodwin,et al.  Adaptive filtering prediction and control , 1984 .

[9]  Lennart Ljung,et al.  Theory and Practice of Recursive Identification , 1983 .