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
An algorithm for clustering cDNAs for gene expression analysis
RECOMB '99 Proceedings of the third annual international conference on Computational molecular biology
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Clustering by pattern similarity in large data sets
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
MaPle: A Fast Algorithm for Maximal Pattern-based Clustering
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)
Bi-k-bi clustering: mining large scale gene expression data using two-level biclustering
International Journal of Data Mining and Bioinformatics
Gene expression network discovery: a pattern based biclustering approach
Proceedings of the 2011 International Conference on Communication, Computing & Security
Discovering non-exclusive functional modules from gene expression data
International Journal of Information and Communication Technology
MFCluster: mining maximal fault-tolerant constant row biclusters in microarray dataset
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Hi-index | 0.00 |
MicroCluster is a deterministic biclustering algorithm that can mine arbitrarily positioned and overlapping clusters of gene expression data to find interesting patterns. Depending on the parameter values, MicroCluster can mine different types of clusters, including those with constant or similar row or column values, as well as scaling and shifting expression patterns. MicroCluster first constructs a range multigraph, a compact representation of all value ranges in the data set that are similar between any two columns. It then searches for constrained maximal cliques in this multigraph to yield the final set of biclusters. Optionally, MicroCluster merges or deletes clusters with large overlaps. Tests on several synthetic and real data sets illustrate MicroCluster's effectiveness.This article is part of a special issue on data mining for bioinformatics.