Partition semantics for incomplete information in relational databases
SIGMOD '88 Proceedings of the 1988 ACM SIGMOD international conference on Management of data
Machine Learning
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Data preparation for data mining
Data preparation for data mining
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Computing Full and Iceberg Datacubes Using Partitions
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Efficient Mining of Constrained Correlated Sets
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Efficient mining of both positive and negative association rules
ACM Transactions on Information Systems (TOIS)
A Thorough Experimental Study of Datasets for Frequent Itemsets
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
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In this paper, two concepts are introduced: decision correlation rules and contingency vectors. The first concept results from a cross fertilization between correlation and decision rules. It enables relevant links to be highlighted between sets of patterns of a binary relation and the values of target items belonging to the same relation on the twofold basis of the Chi-Squared measure and of the support of the extracted patterns. Due to the very nature of the problem, levelwise algorithms only allow extraction of results with long execution times and huge memory occupation. To offset these two problems, we propose an algorithm based both on the lectic order and contingency vectors, an alternate representation of contingency tables.