Chapter

Rough Sets and Knowledge Technology

Volume 4481 of the series Lecture Notes in Computer Science pp 284-291

Evolutionary Fuzzy Biclustering of Gene Expression Data

  • Sushmita MitraAffiliated withMachine Intelligence Unit, Indian Statistical Institute, Kolkata 700 108
  • , Haider BankaAffiliated withCentre for Soft Computing Research, Indian Statistical Institute, Kolkata 700 108
  • , Jiaul Hoque PaikAffiliated withMachine Intelligence Unit, Indian Statistical Institute, Kolkata 700 108

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Abstract

Biclustering or simultaneous clustering attempts to find maximal subgroups of genes and subgroups of conditions where the genes exhibit highly correlated activities over a range of conditions. The possibilistic approach extracts one bicluster at a time, by assigning to it a membership for each gene-condition pair. In this study, a novel evolutionary framework is introduced for generating optimal fuzzy possibilistic biclusters from microarray gene expression data. The different parameters controlling the size of the biclusters are tuned. The experimental results on benchmark datasets demonstrate better performance as compared to existing algorithms available in literature.

Keywords

Microarray Genetic algorithms Possibilistic clustering Optimization