Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Depth first generation of long patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A tree projection algorithm for generation of frequent item sets
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Clustering by pattern similarity in large data sets
Proceedings of the 2002 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
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
Efficiently Mining Maximal Frequent Itemsets
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Handling very large numbers of association rules in the analysis of microarray data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Enhanced Biclustering on Expression Data
BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
MaPle: A Fast Algorithm for Maximal Pattern-based Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Biclustering in Gene Expression Data by Tendency
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
MicroCluster: Efficient Deterministic Biclustering of Microarray Data
IEEE Intelligent Systems
BicAT: a biclustering analysis toolbox
Bioinformatics
New cluster ensemble approach to integrative biological data analysis
International Journal of Data Mining and Bioinformatics
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Due to the increase in gene expression data sets in recent years, various data mining techniques have been proposed for mining gene expression profiles. However, most of these methods target single gene expression data sets and cannot handle all the available gene expression data in public databases in reasonable amount of time and space. In this paper, we propose a novel framework, bi-k-bi clustering, for finding association rules of gene pairs that can easily operate on large scale and multiple heterogeneous data sets. We applied our proposed framework on the available NCBI GEO Homo sapiens data sets. Our results show consistency and relatedness with the available literature and also provides novel associations.