An improved method for fuzzy clustering
Sevinç İlhan Omurca, N. Jeremi Duru · 2009
Adaptive resonance theory (ART) is an unsupervised neural network. Fuzzy ART is a variation of ART, allows both binary and analogue input patterns. However, Fuzzy ART has the cluster overlapping problem. In this study, to solve this problem, we propose a new improved fuzzy ART (IFART) algorithm. In the proposed algorithm, after the clusters are formed, membership degrees of each data instance to all clusters are calculated according to the cluster centers. If data instances are not in the cluster with maximum membership degree, then they are moved between clusters according to their maximum membership degrees. The clustering results on real sample datasets are investigated and compared with the conventional Fuzzy ART. It is seen that, Improved Fuzzy ART is more efficient then Fuzzy ART and also a high performance algorithm than SOM.