A new validation index for determining the number of clusters in a data set
Haojun Sun, Shengrui Wang, Qingshan Jiang · 2002
Clustering analysis plays an important role in solving practical problems in such domains as data mining in large databases. In this paper, we are interested in fuzzy c-means (FCM) based algorithms. The main purpose is to design an effective validity function to measure the result of clustering and detecting the best number of clusters for a given data set in practical applications. After a review of the relevant literature, we present the new validity function. Experimental results and comparisons will be given to illustrate the performance of the new validity function.