Title :
Comparison on Bayesian Ying-Yang theory based clustering number selection criterion with information theoretical criteria
Author :
Lai, Z.B. ; Guo, P. ; Wang, T.J. ; Xu, L.
Author_Institution :
Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, Shatin, Hong Kong
Abstract :
A criterion based on the Bayesian Ying Yang learning theory and system was proposed by Xu (1995, 1996, 1997) for selecting the number of clusters in the clustering analysis and the number of Gaussians in a finite mixture model. In this paper we compare the performance of this criterion with other existing cluster number selection criteria such as Akaike´s information criterion (AIC), CAIC, etc
Keywords :
Bayes methods; information theory; learning (artificial intelligence); number theory; statistical analysis; Akaike information criterion; Bayesian Ying-Yang theory; EM algorithm; clustering number selection; information theoretical criteria; learning theory; Bayesian methods; Clustering algorithms; Clustering methods; Computer science; Data analysis; Information analysis; Mean square error methods; Pattern recognition; Statistical analysis; Supervised learning;
Conference_Titel :
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
0-7803-4859-1
DOI :
10.1109/IJCNN.1998.682370
