Fuzzy C-means method for clustering microarray data | Zendy

Doulaye Dembélé | Zendy; Philippe Kastner | Zendy

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Fuzzy C-means method for clustering microarray data

Author(s) -

Doulaye Dembélé,

Philippe Kastner

Publication year - 2003

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btg119

Subject(s) - cluster analysis , data mining , fuzzy logic , computer science , set (abstract data type) , fuzzy clustering , determining the number of clusters in a data set , data set , gene chip analysis , cluster (spacecraft) , fuzzy set , microarray analysis techniques , single linkage clustering , pattern recognition (psychology) , dna microarray , artificial intelligence , gene , cure data clustering algorithm , biology , genetics , gene expression , programming language

Clustering analysis of data from DNA microarray hybridization studies is essential for identifying biologically relevant groups of genes. Partitional clustering methods such as K-means or self-organizing maps assign each gene to a single cluster. However, these methods do not provide information about the influence of a given gene for the overall shape of clusters. Here we apply a fuzzy partitioning method, Fuzzy C-means (FCM), to attribute cluster membership values to genes.

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