Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Clustering by pattern similarity in large data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
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
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
d-Clusters: Capturing Subspace Correlation in a Large Data Set
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Biclustering Models for Structured Microarray Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Shifting and scaling patterns from gene expression data
Bioinformatics
BicAT: a biclustering analysis toolbox
Bioinformatics
Improved possibilistic C-means clustering algorithms
IEEE Transactions on Fuzzy Systems
New spectral methods for ratio cut partitioning and clustering
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Techniques for clustering gene expression data
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
Evolutionary metaheuristic for biclustering based on linear correlations among genes
Proceedings of the 2010 ACM Symposium on Applied Computing
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
Measuring the quality of shifting and scaling patterns in biclusters
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Discovering non-exclusive functional modules from gene expression data
International Journal of Information and Communication Technology
An effective measure for assessing the quality of biclusters
Computers in Biology and Medicine
BiMine+: An efficient algorithm for discovering relevant biclusters of DNA microarray data
Knowledge-Based Systems
A new measure for gene expression biclustering based on non-parametric correlation
Computer Methods and Programs in Biomedicine
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Biclustering has emerged as an important method for analyzing gene expression data from microarray technology. It allows to identify groups of genes which behave similarly under a subset of conditions. As a gene may play more than one biological role in conjunction with distinct groups of genes, non-exclusive biclustering algorithms are required. In this paper we propose a new method to obtain potentially-overlapping biclusters, the Possibilistic Spectral Biclustering algorithm (PSB), based on Fuzzy Technology and Spectral Clustering. We tested our method on S. cerevisiae cell cycle expression data and on a human cancer dataset, validating the obtained biclusters using known classifications of conditions and GO Term Finder for functional annotations of genes. Results are available at http://decsai.ugr.es/~ccano/psb.