CURE: an efficient clustering algorithm for large databases
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Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering by pattern similarity in large data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Biclustering of Expression Data
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VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
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VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Enhanced Biclustering on Expression Data
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MaPle: A Fast Algorithm for Maximal Pattern-based Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining Deterministic Biclusters in Gene Expression Data
BIBE '04 Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering
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SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
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IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Data Mining: Concepts and Techniques
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ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Quick Hierarchical Biclustering on Microarray Gene Expression Data
BIBE '06 Proceedings of the Sixth IEEE Symposium on BionInformatics and BioEngineering
Shifting and scaling patterns from gene expression data
Bioinformatics
BicAT: a biclustering analysis toolbox
Bioinformatics
Multi-objective evolutionary biclustering of gene expression data
Pattern Recognition
Maximal Subspace Coregulated Gene Clustering
IEEE Transactions on Knowledge and Data Engineering
An association analysis approach to biclustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
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An approach to find embedded clusters using density based techniques
ICDCIT'05 Proceedings of the Second international conference on Distributed Computing and Internet Technology
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Co-regulation is a common phenomenon in gene expression. Finding positively and negatively co-regulated gene clusters from gene expression data is a real need. Existing techniques based on global similarity are unable to detect true up- and down-regulated gene clusters. This paper presents an expression pattern based biclustering technique, CoBi, for grouping both positively and negatively regulated genes from microarray expression data. Regulation pattern and similarity in degree of fluctuation are accounted for while computing similarity between two genes. Unlike traditional biclustering techniques, which use greedy iterative approaches, it uses a BiClust tree that needs single pass over the entire dataset to find a set of biologically relevant biclusters. Biclusters determined from different gene expression datasets by the technique show highly enriched functional categories.