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
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Co-clustering documents and words using bipartite spectral graph partitioning
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
A Monte Carlo algorithm for fast projective clustering
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
Center CLICK: A Clustering Algorithm with Applications to Gene Expression Analysis
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
Comparing Subspace Clusterings
IEEE Transactions on Knowledge and Data Engineering
Finding biclusters by random projections
Theoretical Computer Science
Algorithms and theory of computation handbook
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A coCluster of a m x n matrix X is a submatrix determined by a subset of the rows and a subset of the columns. The problem of finding coClusters with specific properties is of interest, in particular, in the analysis of microarray experiments. In that case the entries of the matrix X are the expression levels of $m$ genes in each of $n$ tissue samples. One goal of the analysis is to extract a subset of the samples and a subset of the genes, such that the expression levels of the chosen genes behave similarly across the subset of the samples, presumably reflecting an underlying regulatory mechanism governing the expression level of the genes.We propose to base the similarity of the genes in a coCluster on a simple biological model, in which the strength of the regulatory mechanism in sample j is Hj, and the response strength of gene i to the regulatory mechanism is Gi. In other words, every two genes participating in a good coCluster should have expression values in each of the participating samples, whose ratio is a constant depending only on the two genes. Noise in the expression levels of genes is taken into account by allowing a deviation from the model, measured by a relative error criterion. The sleeve-width of the coCluster reflects the extent to which entry i,j in the coCluster is allowed to deviate, relatively, from being expressed as the product GiHj.We present a polynomial-time Monte-Carlo algorithm which outputs a list of coClusters whose sleeve-widths do not exceed a prespecified value. Moreover, we prove that the list includes, with fixed probability, a coCluster which is near-optimal in its dimensions. Extensive experimentation with synthetic data shows that the algorithm performs well.