Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining for Strong Negative Associations in a Large Database of Customer Transactions
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
A New and Versatile Method for Association Generation
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Mining Minimal Non-redundant Association Rules Using Frequent Closed Itemsets
CL '00 Proceedings of the First International Conference on Computational Logic
Efficient mining of both positive and negative association rules
ACM Transactions on Information Systems (TOIS)
Generalized disjunction-free representation of frequents patterns with at most k negations
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Efficient mining of dissociation rules
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Adequate condensed representations of patterns
Data Mining and Knowledge Discovery
Non-Derivable Item Set and Non-Derivable Literal Set Representations of Patterns Admitting Negation
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Computing Implications with Negation from a Formal Context
Fundamenta Informaticae - Concept Lattices and Their Applications
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In association rules mining, current trend is witnessing the emergence of a growing number of works toward bringing negative items to light in the mined knowledge. However, the amount of the extracted rules is huge, thus not feasible in practice. In this paper, we propose to extract a subset of generalized association rules (i.e., association rules with negation) from which we can retrieve the whole set of generalized association rules. Results of experiments carried out on benchmark databases showed important profits in terms of compactness of the introduced generic basis.