A max-min learning rule for Fuzzy ART

Abstract

Fuzzy Adaptive Resonance Theory (Fuzzy ART) is an unsupervised neural network, which clusters data effectively based on learning from training data. In the learning process, Fuzzy ARTs update the weight vector of the wining category based on the current input pattern from training data. Fuzzy ARTs, however, only learn from patterns whose values are smaller than values of stored patterns. In this paper, we propose a max-min learning rule of Fuzzy ART that learns all patterns of training data and reduces effect of abnormal training patterns. Our learning rule changes the weight vector of the wining category based on the minimal difference between the current input pattern and the old weight vector of the wining category. We have also conducted experiments on seven benchmark datasets to prove the effectiveness of the proposed learning rule. Experiment results show that clustering results of Fuzzy ART with our learning rule (Max-min Fuzzy ART) is significantly higher than that of other models in complex datasets.