An Enhancing Dynamic Self-Organizing Map for Data Clustering

被引：0

作者：

Wang, Ting ^{[1
,2
]}

Yu, Xinghuo ^{[3
]}

Alahakoon, Damminda ^{[4
]}

Fei, Shumin ^{[2
]}

机构：

[1] RMIT Univ, Sch Elect & Comp Sci, Melbourne, Vic 3167, Australia

[2] Southeast Univ, Sch Automat, Nanjing 210096, Jiangsu, Peoples R China

[3] RMIT Univ, Platform Technologies Res Inst, Melbourne, Vic 3167, Australia

[4] Monash Univ, Clayton Sch Informat Technol, Clayton, Vic 3800, Australia

来源：

2013 10TH IEEE INTERNATIONAL CONFERENCE ON CONTROL AND AUTOMATION (ICCA) | 2013年

基金：

中国国家自然科学基金;

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a novel growing self-organizing map which features incremental learning, dynamic network structure and good visualization ability. It allows for on-line and continuous learning on both static and evolving data distributions. The experiments are carried out on some benchmark data sets for vector quantisation and clustering. Compared with the GSOM method, our results show that this new model can achieve better or comparable performance in real-world data sets.

引用

页码：1324 / 1329

页数：6

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