Document Type

Article

Publication Date

June 2006

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 annotations, more accurately than if using one type alone.

Comments

This article originally appeared in: Andreopoulos, B., An, A., and Wang, X. (2006). Bi-level Clustering of Mixed Categorical and Numerical Biomedical Data. International Journal of Data Mining and Bioinformatics, 1(1), 19-56. Copyright © 2006 Inderscience Enterprises Ltd. The article can also be found online at this link.

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