Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 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
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
Shifting and scaling patterns from gene expression data
Bioinformatics
MIB: Using mutual information for biclustering gene expression data
Pattern Recognition
BARTMAP: A viable structure for biclustering
Neural Networks
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This paper presents a pattern aided subspace clustering algorithm (PATSUB+) to find biologically relevant coexpressed patterns in gene expression data. The algorithm works iteratively over a relative expression domain using NMRS (Normalized Mean Residue Similarity) measure, it discovers a set of possibly overlapping subspace clusters for any gene expression matrix. PATSUB+ performs satisfactorily while tested on several real life gene expression datasets and compared with several competing algorithms.