A NOVEL VALIDITY INDEX FOR THE SUBTRACTIVE CLUSTERING ALGORITHM
Horng-Lin Shieh, Cheng‐Chien Kuo · International Journal of Pattern Recognition and Artificial Intelligence · 2011
This paper proposes a new validity index for the subtractive clustering (SC) algorithm. The subtractive clustering algorithm proposed by Chiu is an effective and simple method for identifying the cluster centers of sampling data based on the concept of a density function. The SC algorithm continually produces the cluster centers until the final potential compared with the original is less than a predefined threshold. The procedure is terminated when there are only a few data points around the most recent cluster. The choice of the threshold is an important factor affecting the clustering results: if it is too large, then too few data points will be accepted as cluster centers; if it is too small, then too many cluster centers will be generated. In this paper, a modified SC algorithm for data clustering based on a cluster validity index is proposed to obtain the optimal number of clusters. Six examples show that the proposed index achieves better performance results than other cluster validities do.