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Summary: Int. J. Data Mining and Bioinformatics, Vol. 1, No. 1, 2006 19
Copyright © 2006 Inderscience Enterprises Ltd.
Bi-level clustering of mixed categorical and numerical
biomedical data
Bill Andreopoulos* and Aijun An
Department of Computer Science and Engineering,
York University, M3J1P3, Toronto, Ontario, Canada
E-mail: billa@cs.yorku.ca E-mail: aan@cs.yorku.ca
*Corresponding author
Xiaogang Wang
Department of Mathematics and Statistics,
York University, M3J1P3, Toronto, Ontario, Canada
E-mail: stevenw@mathstat.yorku.ca
Abstract: Biomedical data sets often have mixed categorical and numerical
types, where the former represent semantic information on the objects and the
latter represent experimental results. We present the BILCOM algorithm for
`Bi-Level Clustering of Mixed categorical and numerical data types'. BILCOM
performs a pseudo-Bayesian process, where the prior is categorical clustering.
BILCOM partitions biomedical data sets of mixed types, such as hepatitis,
thyroid disease and yeast gene expression data with Gene Ontology
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