A deterministic annealing approach to clustering
Pattern Recognition Letters
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Self-Organizing Maps
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Enhanced Biclustering on Expression Data
BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
The maximum edge biclique problem is NP-complete
Discrete Applied Mathematics
Mining Deterministic Biclusters in Gene Expression Data
BIBE '04 Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Biclustering of Expression Data Using Simulated Annealing
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
Multi-Metric and Multi-Substructure Biclustering Analysis for Gene Expression Data
CSB '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference
The possibilistic C-means algorithm: insights and recommendations
IEEE Transactions on Fuzzy Systems
Alternating cluster estimation: a new tool for clustering and function approximation
IEEE Transactions on Fuzzy Systems
An Experimental Validation of Some Indexes of Fuzzy Clustering Similarity
WILF '09 Proceedings of the 8th International Workshop on Fuzzy Logic and Applications
Stability and Performances in Biclustering Algorithms
Computational Intelligence Methods for Bioinformatics and Biostatistics
Evolutionary fuzzy biclustering of gene expression data
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
A novel approach for biclustering gene expression data using modular singular value decomposition
CIBB'09 Proceedings of the 6th international conference on Computational intelligence methods for bioinformatics and biostatistics
CIBB'10 Proceedings of the 7th international conference on Computational intelligence methods for bioinformatics and biostatistics
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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.