An Enhanced ART2 Neural Network for Clustering Analysis

Jianhong Luo; Dezhao Chen

1st International ICST Workshop on Knowledge Discovery and Data Mining

Research Article

An Enhanced ART2 Neural Network for Clustering Analysis

Cite: BibTeX Plain Text

@INPROCEEDINGS{10.4108/wkdd.2008.2693,
    author={Jianhong Luo and Dezhao Chen},
    title={An Enhanced ART2 Neural Network for Clustering Analysis},
    proceedings={1st International ICST Workshop on Knowledge Discovery and Data Mining},
    publisher={ACM},
    proceedings_a={WKDD},
    year={2010},
    month={5},
    keywords={},
    doi={10.4108/wkdd.2008.2693}
}

Jianhong Luo
Dezhao Chen
Year: 2010
An Enhanced ART2 Neural Network for Clustering Analysis
WKDD
ACM
DOI: 10.4108/wkdd.2008.2693

Jianhong Luo¹^,*, Dezhao Chen¹

1: Zhejiang University, Hang Zhou, 310027, People’s Republic of China

*Contact email: luojianhong@gmail.com

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.

Published: 2010-05-16
Publisher: ACM
Modified: 2010-05-16

: http://dx.doi.org/10.4108/wkdd.2008.2693