HDGSOM: A modified growing self-organizing map for high dimensional data clustering

被引：0

作者：

Amarastri, R ^{[1
]}

Alahakoon, D ^{[1
]}

Smith, KA ^{[1
]}

机构：

[1] Monash Univ, Sch Business Syst, Clayton, Vic 3168, Australia

来源：

HIS'04: Fourth International Conference on Hybrid Intelligent Systems, Proceedings | 2005年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The Growing Self Organizing Map (GSOM) algorithm is a variant of the Self Organizing Map (SOM), It has a dynamically growing structure that adapts to the natural structure of the data. It has been identified that the growing of the GSOM can get negatively affected when used with very large dimensional data such as those in text and DNA data sets. This paper addresses these issues and presents a modified version of the GSOM called the High Dimensional GSOM (HDGSOM). The algorithm and experimental results showing the improved performance of the HDGSOM are also presented.

引用

页码：216 / 221

页数：6

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