An ensemble method of discovering sample classes using gene expression profiling

Dechang Chen, Zhe Zhang, Zhenqiu Liu, Xiuzhen Cheng

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Scopus citations


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.

Original languageEnglish
Title of host publicationSpringer Optimization and Its Applications
PublisherSpringer International Publishing
Number of pages8
StatePublished - 2007
Externally publishedYes

Publication series

NameSpringer Optimization and Its Applications
ISSN (Print)1931-6828
ISSN (Electronic)1931-6836


  • Cluster analysis
  • Dissimilarity measure
  • Gene expression


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