Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS
Volume 13, Issue 3, Pages 757-763Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNN.2002.1000144
Keywords
bootstrap; cluster number selection; data smoothing; SEM algorithm; small number sample set; smoothing parameter estimation
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One major problem in cluster analysis is the determination 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-Kullback 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.
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