Data Clustering Based on Improved Algorithm of ART2
Jin-Liang Wang · Journal of Lanzhou Jiaotong University · 2008
By analyzing the clustering process of classical Adaptive Resonance Theory Network(ART2),this paper points out the shortcomings of ART2:subjective setting of vigilance parameter,excessive dependence on winning neuron information and output without hierarchical structure,etc.So an improved clustering algorithm of ART2 has been presented.This algorithm can realize multi-layer dynamic clustering structure through a single ART2 neural network by simultaneously taking into consideration the information from both the winning neuron and other neurons in process of competition as well as Hebb rule.Therefore,there is no demand of retraining neural network within a certain range of granularity.Besides,this algorithm reduces the requirement of subjective setting of vigilance parameter.The above mentioned advantages can effectively satisfy fundamental demands of clustering and get rid of the problems of performance and parameter setting caused by employing cascade structure to realize hierarchical clustering structure.