Class discovery in gene expression data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
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
Interactive exploration of coherent patterns in time-series gene expression data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining phenotypes and informative genes from gene expression data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A rank sum test method for informative gene discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining top-K covering rule groups for gene expression data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
TRICLUSTER: an effective algorithm for mining coherent clusters in 3D microarray data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
An Interactive Approach to Mining Gene Expression Data
IEEE Transactions on Knowledge and Data Engineering
Mining frequent closed cubes in 3D datasets
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
GPX: interactive mining of gene expression data
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Maximal Subspace Coregulated Gene Clustering
IEEE Transactions on Knowledge and Data Engineering
Bregman bubble clustering: A robust framework for mining dense clusters
ACM Transactions on Knowledge Discovery from Data (TKDD)
Closed patterns meet n-ary relations
ACM Transactions on Knowledge Discovery from Data (TKDD)
BioDM'06 Proceedings of the 2006 international conference on Data Mining for Biomedical Applications
Mining maximal correlated member clusters in high dimensional database
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Mining biologically significant co-regulation patterns from microarray data
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Mining maximal local conserved gene clusters from microarray data
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
A general approach to mining quality pattern-based clusters from microarray data
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
Neighborhood-Based clustering of gene-gene interactions
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Mining of temporal coherent subspace clusters in multivariate time series databases
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
A unified adaptive co-identification framework for high-d expression data
PRIB'12 Proceedings of the 7th IAPR international conference on Pattern Recognition in Bioinformatics
A survey on enhanced subspace clustering
Data Mining and Knowledge Discovery
MicroClAn: Microarray clustering analysis
Journal of Parallel and Distributed Computing
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Extensive studies have shown that mining microarray data sets is important in bioinformatics research and biomedical applications. In this paper, we explore a novel type of gene-sample-time microarray data sets, which records the expression levels of various genes under a set of samples during a series of time points. In particular, we propose the mining of coherent gene clusters from such data sets. Each cluster contains a subset of genes and a subset of samples such that the genes are coherent on the samples along the time series. The coherent gene clusters may identify the samples corresponding to some phenotypes (e.g., diseases), and suggest the candidate genes correlated to the phenotypes. We present two efficient algorithms, namely the Sample-Gene Search and the Gene-Sample Search, to mine the complete set of coherent gene clusters. We empirically evaluate the performance of our approaches on both a real microarray data set and synthetic data sets. The test results have shown that our approaches are both efficient and effective to find meaningful coherent gene clusters.