Evolutionary Fuzzy Biclustering of Gene Expression Data

  • Sushmita Mitra
  • Haider Banka
  • Jiaul Hoque Paik
Conference paper

DOI: 10.1007/978-3-540-72458-2_35

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4481)
Cite this paper as:
Mitra S., Banka H., Paik J.H. (2007) Evolutionary Fuzzy Biclustering of Gene Expression Data. In: Yao J., Lingras P., Wu WZ., Szczuka M., Cercone N.J., Ślȩzak D. (eds) Rough Sets and Knowledge Technology. RSKT 2007. Lecture Notes in Computer Science, vol 4481. Springer, Berlin, Heidelberg

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 

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Sushmita Mitra
    • 1
  • Haider Banka
    • 2
  • Jiaul Hoque Paik
    • 1
  1. 1.Machine Intelligence Unit, Indian Statistical Institute, Kolkata 700 108India
  2. 2.Centre for Soft Computing Research, Indian Statistical Institute, Kolkata 700 108India

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