Clustering analysis for gene expression data: A methodological review

Author

Rui Fa ; Nandi, A.K. ; Li-Yun Gong

Author_Institution

Dept. of Electr. Eng. & Electron., Univ. of Liverpool, Liverpool, UK

fYear

2012

fDate

2-4 May 2012

Firstpage

1

Lastpage

6

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

biology computing; fuzzy set theory; genetics; lab-on-a-chip; unsupervised learning; clustering analysis; ensemble clustering; fuzzy clustering; kernel-based clustering; merging clustering; microarray gene expression data analysis; self-organizing clustering; self-splitting clustering; unsupervised learning; Algorithm design and analysis; Benchmark testing; Clustering algorithms; Gene expression; Kernel; Oscillators; Partitioning algorithms; Clustering algorithm; Clustering validation; Microarray gene expression data analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Communications Control and Signal Processing (ISCCSP), 2012 5th International Symposium on

Conference_Location

Rome

Print_ISBN

978-1-4673-0274-6

Type

conf

DOI

10.1109/ISCCSP.2012.6217811

Filename

6217811