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
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)
TRICLUSTER: an effective algorithm for mining coherent clusters in 3D microarray data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Biclustering of Expression Data with Evolutionary Computation
IEEE Transactions on Knowledge and Data Engineering
Mining Shifting-and-Scaling Co-Regulation Patterns on Gene Expression Profiles
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Multi-objective evolutionary biclustering of gene expression data
Pattern Recognition
EvoOligo: oligonucleotide probe design with multiobjective evolutionary algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Discriminative orthogonal neighborhood-preserving projections for classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Efficiently mining time-delayed gene expression patterns
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Parallel identification of gene biclusters with coherent evolutions
IEEE Transactions on Signal Processing - Part II
Application of Simulated Annealing to the Biclustering of Gene Expression Data
IEEE Transactions on Information Technology in Biomedicine
Discriminant Locally Linear Embedding With High-Order Tensor Data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Optimized graph-based segmentation for ultrasound images
Neurocomputing
Scaling up cosine interesting pattern discovery: A depth-first method
Information Sciences: an International Journal
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The analysis of gene expression data obtained from microarray experiments is important for discovering the biological process of genes. Biclustering algorithms have been proven to be able to group the genes with similar expression patterns under a number of experimental conditions. In this paper, we propose a new biclustering algorithm based on evolutionary learning. By converting the biclustering problem into a common clustering problem, the algorithm can be applied in a search space constructed by the conditions. To further reduce the size of the search space, we randomly separate the full conditions into a number of condition subsets (subspaces), each of which has a smaller number of conditions. The algorithm is applied to each subspace and is able to discover bicluster seeds within a limited computing time. Finally, an expanding and merging procedure is employed to combine the bicluster seeds into larger biclusters according to a homogeneity criterion. We test the performance of the proposed algorithm using synthetic and real microarray data sets. Compared with several previously developed biclustering algorithms, our algorithm demonstrates a significant improvement in discovering additive biclusters.