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

Authors:
Satoru Kato;Tadashi Horiuchi;Yoshio Itoh
Affiliations:
Matsue College of Technology, Shimane, Japan 690-8518;Matsue College of Technology, Shimane, Japan 690-8518;Tottori University, Tottori, Japan 680-8550
Venue:
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Year:
2009

Citing 2
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Abstract

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.