Parameterless clustering techniques for gene expression analysis

Vincent Shin-Mu Tseng, Ching Pin Kao

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


In recent years, clustering analysis has even become a valuable and useful tool for insilico analysis of microarray or gene expression data. Although a number of clustering methods have been proposed, they are confronted with difficulties in meeting the requirements of automation, high quality, and high efficiency at the same time. In this chapter, we discuss the issue of parameterless clustering technique for gene expression analysis. We introduce two novel, parameterless and efficient clustering methods that fit for analysis of gene expression data. The unique feature of our methods is they incorporate the validation techniques into the clustering process so that high quality results can be obtained. Through experimental evaluation, these methods are shown to outperform other clustering methods greatly in terms of clustering quality, efficiency, and automation on both of synthetic and real data sets.

Original languageEnglish
Title of host publicationAdvanced Data Mining Technologies in Bioinformatics
PublisherIGI Global
Number of pages19
ISBN (Print)9781591408635
StatePublished - 1 Dec 2006

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