On the study of BKYY cluster number selection criterion for small sample data set with bootstrap technique

Author

Guo, Ping ; Xu, Lei

Author_Institution

Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, Shatin, Hong Kong

Volume

2

fYear

1999

fDate

36342

Firstpage

965

Abstract

The Bayesian-Kullback ying-yang (BKYY) learning theory and system has been proposed by Xu (1995, 1997), and one special case of ying-yang system can provide the model selection criteria for selecting the number of clusters in the clustering analysis. In this paper, we present an experimental study of this cluster number selection criterion in a small number sample set case. The results show that the criterion performed reasonable well when mixture parameters were estimated by incorporating a bootstrap technique with the EM algorithm

Keywords

Bayes methods; computer bootstrapping; learning (artificial intelligence); maximum likelihood estimation; neural nets; pattern recognition; Bayesian ying-yang learning; Bayesian-Kullback scheme; EM algorithm; bootstrap; cluster number selection; clustering analysis; learning system; maximum likelihood estimation; model selection; sample data set; Bayesian methods; Clustering algorithms; Computer science; Data analysis; Data engineering; Electronic mail; Maximum likelihood estimation; Parameter estimation; Partitioning algorithms; Supervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.831084

Filename

831084