Document Type
Article
Publication Date
June 2006
Publication Title
International Journal of Data Mining and Bioinformatics
Volume
1
Issue Number
1
First Page
19
Last Page
56
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.
Recommended Citation
Bill Andreopoulos, Aijun An, and Xiaogang Wang. "Bi-level Clustering of Mixed Categorical and Numerical Biomedical Data" International Journal of Data Mining and Bioinformatics (2006): 19-56. https://doi.org/10.1504/IJDMB.2006.009920
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.