Fast discovery of association rules
Advances in knowledge discovery and data mining
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficient mining of association rules using closed itemset lattices
Information Systems
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns with counting inference
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries
Data Mining and Knowledge Discovery
TFP: An Efficient Algorithm for Mining Top-K Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
Mining top-K covering rule groups for gene expression data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
On discovery of maximal confident rules without support pruning in microarray data
Proceedings of the 5th international workshop on Bioinformatics
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Constraint-based concept mining and its application to microarray data analysis
Intelligent Data Analysis
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Top-down mining of frequent closed patterns from very high dimensional data
Information Sciences: an International Journal
Finding closed frequent item sets by intersecting transactions
Proceedings of the 14th International Conference on Extending Database Technology
Mining formal concepts with a bounded number of exceptions from transactional data
KDID'04 Proceedings of the Third international conference on Knowledge Discovery in Inductive Databases
Database transposition for constrained (closed) pattern mining
KDID'04 Proceedings of the Third international conference on Knowledge Discovery in Inductive Databases
TP+close: mining frequent closed patterns in gene expression datasets
VDMB'06 Proceedings of the First international conference on Data Mining and Bioinformatics
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
Local pattern discovery in Array-CGH data
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
Contribution to gene expression data analysis by means of set pattern mining
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Mining frequent δ-free patterns in large databases
DS'05 Proceedings of the 8th international conference on Discovery Science
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
DisClose: discovering colossal closed itemsets via a memory efficient compact row-tree
PAKDD'12 Proceedings of the 2012 Pacific-Asia conference on Emerging Trends in Knowledge Discovery and Data Mining
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We analyze expression matrices to identify a priori interesting sets of genes, e.g., genes that are frequently co-regulated. Such matrices provide expression values for given biological situations (the lines) and given genes (columns). The frequent itemset (sets of columns) extraction technique enables to process difficult cases (millions of lines, hundreds of columns) provided that data is not too dense. However, expression matrices can be dense and have generally only few lines w.r.t. the number of columns. Known algorithms, including the recent algorithms that compute the so-called condensed representations can fail. Thanks to the properties of Galois connections, we propose an original technique that processes the transposed matrices while computing the sets of genes. We validate the potential of this framework by looking for the closed sets in two microarray data sets.