Clustering by pattern similarity in large data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Discovering local structure in gene expression data: the order-preserving submatrix problem
Proceedings of the sixth annual international conference on Computational biology
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
DHC: A Density-Based Hierarchical Clustering Method for Time Series Gene Expression Data
BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
OP-Cluster: Clustering by Tendency in High Dimensional Space
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
HARP: A Practical Projected Clustering Algorithm
IEEE Transactions on Knowledge and Data Engineering
Shifting and scaling patterns from gene expression data
Bioinformatics
A Biclustering Method to Discover Co-regulated Genes Using Diverse Gene Expression Datasets
BICoB '09 Proceedings of the 1st International Conference on Bioinformatics and Computational Biology
Biclusters evaluation based on shifting and scaling patterns
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
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Biclustering is a very popular method to identify hidden co-regulation patterns among genes. There are numerous biclustering algorithms designed to undertake this challenging task, however, a thorough comparison between these algorithms is even harder to accomplish due to lack of a ground truth and large variety in the search strategies and objectives of the algorithms. In this paper, we address this less studied, yet important problem and formally analyze several biclustering algorithms in terms of the bicluster patterns they attempt to discover. We systematically formulate the requirements for well-known patterns and show the constraints imposed by biclustering algorithms that determine their capacity to identify such patterns. We also give experimental results from a carefully designed testbed to evaluate the power of the employed search strategies. Furthermore, on a set of real datasets, we report the biological relevance of clusters identified by each algorithm.