A New Fuzzy Clustering Algorithm with Entropy Index Constraint

The fuzziness index m plays an important role in the clustering result of fuzzy clustering algorithms. In order to avoid the fuzziness index m of the CA (competitive agglomeration) clustering algorithm based on FCM (fuzzy C-means) clustering algorithm framework being forced to fix at the usual value 2, a more universal fuzzy clustering algorithm is proposed. Firstly, a fuzzy clustering algorithm named EIC-FCM (entropy index constraint FCM), which has comparable clustering performance to the classical FCM algorithm, is presented by introducing an entropy index r into constraints with m=1. The successful introducing of entropy index r effectively makes the fuzziness index constraint m>1 transform into entropy index constraint 0