Clustering analysis for gene expression data: a methodological review

Fa, Rui and K.Nandi, Asoke and Gong, Liyun (2012) Clustering analysis for gene expression data: a methodological review. In: Communications Control and Signal Processing (ISCCSP), 2012 5th International Symposium on, 2 - 4 May 2012, Aula Magna of the Universita degli Studi Roma TRE Roma, Italy.

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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

Abstract

Clustering is one of most useful tools for the microarray gene expression data analysis. Although there have been many reviews and surveys in the literature, many good and effective clustering ideas have not been collected in a systematic way for some reasons. In this paper, we review five clustering families representing five clustering concepts rather than five algorithms. We also review some clustering validations and collect a list of benchmark gene expression datasets.

Keywords:Clustering algorithms, Algorithm design and analysis, Gene expression, Kernel, Oscillators, Partitioning algorithms, Benchmark testing
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
Divisions:College of Science > School of Computer Science
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ID Code:28873
Deposited On:03 Oct 2017 11:50

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