Estimating the number of clusters using multivariate location test statistics

Authors:
Kyungmee Choi;Deok-Hwan Kim;Taeryon Choi
Affiliations:
College of Science and Technology, Hongik University at Jochiwon, Korea;Department of Electronics Engineering, Inha University at Incheon, Korea;Department of Mathematics and Statisics, University of Maryland at Baltimore
Venue:
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Year:
2006

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Abstract

In the cluster analysis, to determine the unknown number of clusters we use a criterion based on a classical location test statistic, Hotelling's T2. At each clustering level, its theoretical threshold is studied in view of its statistical distribution and a multiple comparison problem. In order to examine its performance, extensive experiments are done with synthetic data generated from multivariate normal distributions and a set of real image data.