DocumentCode
2905467
Title
Fuzzy biclustering for DNA microarray data analysis
Author
Han, Lixin ; Yan, Hong
Author_Institution
Dept. of Comput. Sci. & Eng., Hohai Univ., Nanjing
fYear
2008
fDate
1-6 June 2008
Firstpage
1132
Lastpage
1138
Abstract
Fuzzy biclustering analysis is a useful tool for identifying relevant subsets of microarray data. This paper proposes a fuzzy biclustering clustering method for microarray data analysis. The method employs a combination of the Nelder-Mead and min-max algorithm to construct hierarchically structured biclustering. The method can automatically identify the groups of genes that show similar expression patterns under a specific subset of the samples.
Keywords
DNA; biology computing; data analysis; fuzzy set theory; minimax techniques; pattern clustering; DNA microarray data analysis; Nelder-Mead algorithm; fuzzy biclustering clustering method; hierarchically structured biclustering; min-max algorithm; Algorithm design and analysis; Clustering algorithms; Clustering methods; DNA; Data analysis; Gene expression; Genetic algorithms; Iterative algorithms; Monitoring; Sorting;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE International Conference on
Conference_Location
Hong Kong
ISSN
1098-7584
Print_ISBN
978-1-4244-1818-3
Electronic_ISBN
1098-7584
Type
conf
DOI
10.1109/FUZZY.2008.4630513
Filename
4630513
Link To Document