An enhanced ART2 neural network for clustering analysis

Authors:
Jianhong Luo;Dezhao Chen
Affiliations:
Zhejiang University, Hang Zhou, People's Republic of China;Zhejiang University, Hang Zhou, People's Republic of China
Venue:
Proceedings of the 1st international conference on Forensic applications and techniques in telecommunications, information, and multimedia and workshop
Year:
2008

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Abstract

The adaptive resonance theory 2 (ART2) neural network exhibits several properties which can be useful in the data mining and which are lacking in most other neural networks. But ART2 has deficiencies that the categories clustered by ART2 are very mutable to slight changes in training conditions. An improved ART2 with enhanced triplex matching mechanism, named as ETM-ART2, is presented to redress the deficiencies. Several tests results show that ETM-ART2 performs better than classic ART2 when applied to clustering tasks. It is an effective improved algorithm and can be applied to a wide variety of problems.