Cluster Number Selection for a Small Set of Samples Using the Bayesian
Ping Guo, Colin Chen, Michael Rung-Tsong Lyu · 2002
One major problem in cluster analysis is the deter- mination of the number of clusters. In this paper, we describe both theoretical and experimental results in determining the cluster number for a small set of samples using the Bayesian-Kull- back Ying-Yang (BYY) model selection criterion. Under the second-order approximation, we derive a new equation for estimating the smoothing parameter in the cost function. Finally, we propose a gradient descent smoothing parameter estimation approach that avoids complicated integration procedure and gives the same optimal result.