Title :
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
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;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.831084
