A Study on Clustering Method by Self-Organizing Map and Information Criteria

被引：0

作者：

Kato, Satoru ^{[1
]}

Horiuchi, Tadashi ^{[1
]}

Itoh, Yoshio ^{[2
]}

机构：

[1] Matsue Coll Technol, 14-4 Nishi Ikuma, Matsue, Shimane 6908518, Japan

[2] Tottori Univ, Tottori, Japan

来源：

NEURAL INFORMATION PROCESSING, PT 2, PROCEEDINGS | 2009年 / 5864卷

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：

In this paper, we propose a clustering method by SOM and information criteria. In this method, initial cluster-candidates are derived by SOM, and then these candidates are merged appropriately based on information criterion such as BIC or AIC (Akaike Information Criterion). Through the clustering experiments for the artificial datasets and UCI Machine Learning Repository's datasets, we confirm that our proposed method can extract clusters more accurately and stably than the SOM-only method.

引用

页码：874 / +

页数：2

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