Efficiently mining gene expression data via a novel parameterless clustering method

S. Tseng*, Ching Pin Kao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

117 Scopus citations


Clustering analysis has been an important research topic in the machine learning field due to the wide applications. In recent years, it has even become a valuable and useful tool for in-silico 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 paper, we propose a novel, parameterless and efficient clustering algorithm, namely, Correlation Search Technique (CST), which fits for analysis of gene expression data. The unique feature of CST is it incorporates the validation techniques into the clustering process so that high quality clustering results can be produced on the fly. Through experimental evaluation, CST is 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
Pages (from-to)355-365
Number of pages11
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number4
StatePublished - 1 Oct 2005


  • Clustering
  • Data mining
  • Machine learning
  • Mining methods and algorithms

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