A new preprocessor to fuzzy c-means algorithm

Abstract

The fuzzy clustering scenario resulting from Fuzzy c-Means Algorithm (FCM) is highly sensitive to input parameters, number of clusters c and randomly initialized fuzzy membership matrix. Traditionally, the optimal fuzzy clustering scenario is arrived by exhaustive, repetitive invocations of FCM for different c values. In this paper, a new preprocessor based on Simplified Fuzzy Min-Max Neural Network (FMMNN) is proposed for FCM. The new preprocessor results in an estimation of the number of clusters in a given dataset and an initialization for fuzzy membership matrix. FCM algorithm with embeded preprocessor is named as FCMPre. Comparative experimental results of FCMPre with FCM based on benchmark datasets, empirically established that FCMPre discovers optimal (or near optimal) fuzzy clustering scenarios without exhaustive invocations of FCM along with obtaining significant computational gains.