An Ensemble Method of Discovering Sample Classes Using Gene Expression Profiling

Dechang Chen (), Zhe Zhang (), Zhenqiu Liu () and Xiuzhen Cheng ()
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Dechang Chen: Uniformed Services University of the Health Sciences
Zhe Zhang: University of North Carolina
Zhenqiu Liu: TATRC
Xiuzhen Cheng: The George Washington University

A chapter in Data Mining in Biomedicine, 2007, pp 39-46 from Springer

Abstract: Abstract Cluster methods have been successfully applied in gene expression data analysis to address tumor classification. Central to cluster analysis is the notion of dissimilarity between the individual samples. In clustering microarray data, dissimilarity measures are often subjective and predefined prior to the use of clustering techniques. In this chapter, we present an ensemble method to define the dissimilarity measure through combining assignments of observations from a sequence of data partitions produced by multiple clusterings. This dissimilarity measure is then subjective and data dependent. We present our algorithm of hierarchical clustering based on this dissimilarity. Experiments on gene expression data are used to illustrate the application of the ensemble method to discovering sample classes.

Keywords: Cluster analysis; Dissimilarity measure; Gene expression (search for similar items in EconPapers)
Date: 2007
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DOI: 10.1007/978-0-387-69319-4_3

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