Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Alternative Interest Measures for Mining Associations in Databases
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
CoMine: Efficient Mining of Correlated Patterns
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Generating a Condensed Representation for Association Rules
Journal of Intelligent Information Systems
Frequent closed itemset based algorithms: a thorough structural and analytical survey
ACM SIGKDD Explorations Newsletter
Data Mining and Knowledge Discovery
Adequate condensed representations of patterns
Data Mining and Knowledge Discovery
Data & Knowledge Engineering
Efficient Discovery of Frequent Correlated Subgraph Pairs
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Compressed disjunction-free pattern representation versus essential pattern representation
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Re-examination of interestingness measures in pattern mining: a unified framework
Data Mining and Knowledge Discovery
Essential patterns: a perfect cover of frequent patterns
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Contrasting correlations by an efficient double-clique condition
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Top-N minimization approach for indicative correlation change mining
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
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In the literature, many works were interested in mining frequent patterns. Unfortunately, these patterns do not offer the whole information about the correlation rate amongst the items that constitute a given pattern since they are mainly interested in appearance frequency. In this situation, many correlation measures have been proposed in order to convey information on the dependencies within sets of items. In this work, we adopt the correlation measure bond, which provides several interesting properties. Motivated by the fact that the number of correlated patterns is often huge while many of them are redundant, we propose a new exact concise representation of frequent correlated patterns associated to this measure, through the definition of a new closure operator. The proposed representation allows not only to efficiently derive the correlation rate of a given pattern but also to exactly offer its conjunctive, disjunctive and negative supports. To prove the utility of our approach, we undertake an empirical study on several benchmark data sets that are commonly used within the data mining community.