Possibilistic Approach to Biclustering: An Application to Oligonucleotide Microarray Data Analysis

  • Maurizio Filippone
  • Francesco Masulli
  • Stefano Rovetta
  • Sushmita Mitra
  • Haider Banka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4210)


The important research objective of identifying genes with similar behavior with respect to different conditions has recently been tackled with biclustering techniques. In this paper we introduce a new approach to the biclustering problem using the Possibilistic Clustering paradigm. The proposed Possibilistic Biclustering algorithm finds one bicluster at a time, assigning a membership to the bicluster for each gene and for each condition. The biclustering problem, in which one would maximize the size of the bicluster and minimizing the residual, is faced as the optimization of a proper functional. We applied the algorithm to the Yeast database, obtaining fast convergence and good quality solutions. We discuss the effects of parameter tuning and the sensitivity of the method to parameter values. Comparisons with other methods from the literature are also presented.


Gene Expression Data Probabilistic Constraint Picard Iteration Possibilistic Approach Frequent Pattern Mining Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Maurizio Filippone
    • 1
  • Francesco Masulli
    • 1
  • Stefano Rovetta
    • 1
  • Sushmita Mitra
    • 2
    • 3
  • Haider Banka
    • 2
  1. 1.DISI, Dept. Computer and Information SciencesUniversity of Genova and CNISMGenovaItaly
  2. 2.Center for Soft Computing: A National FacilityIndian Statistical InstituteKolkataIndia
  3. 3.Machine Intelligence UnitIndian Statistical InstituteKolkataIndia

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