A METHOD OF DATA CLUSTERING BASED ON IMPROVED ALGORITHM OF ART2
After such characteristics as normalization of vector and global vigilance parameter have been analyzed in the clustering process of classical Adaptive Resonance Theory Network (ART2), shortcomings of ART2 are pointed out, which are inapplicability to the situation correlative with vector modulus, inability of dividing space with different granularities according to the densities of space and output without hierarchical structure. So an improved algorithm of ART2 has been presented in this paper. This algorithm presents local vigilance parameter and pre-selection of neurons with standard of modulus of vector, and obtains dynamic clustering structure with hierarchy structure that is correlative with modulus of vector by cycling. This algorithm also reduces the requirement of setting vigilance parameter, that is, retraining neural network is not needed for clustering with bigger granularity. Such advantages can effectively satisfy basic demands of clustering and be adapted to the environment of clustering of sub-spaces respectively with characteristic of modulus.
Adaptive resonance Neural network Clustering Vigilance parameter
XIAO-DONG QIAN
School of Managent, Lanzhou Jiaotong University, Lanzhou,730070,China
国际会议
2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)
昆明
英文
2640-2645
2008-07-12（万方平台首次上网日期，不代表论文的发表时间）