Article
Bi-level Clustering of Mixed Categorical and Numerical Biomedical Data
International Journal of Data Mining and Bioinformatics
(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.
Keywords
- bi-level clustering,
- categorical,
- numerical data,
- nominal,
- ordinal,
- biomedical data sets,
- Bayesian,
- data mining,
- bioinformatics,
- semantic information,
- experimental results,
- hepatitis,
- thyroid disease,
- yeast gene expression,
- gene ontology
Disciplines
Publication Date
June 2, 2006
DOI
10.1504/IJDMB.2006.009920
Publisher Statement
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.
Citation Information
Bill Andreopoulos, Aijun An and Xiaogang Wang. "Bi-level Clustering of Mixed Categorical and Numerical Biomedical Data" International Journal of Data Mining and Bioinformatics Vol. 1 Iss. 1 (2006) p. 19 - 56 Available at: http://works.bepress.com/william-andreopoulos/21/