Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Concise Representation of Frequent Patterns Based on Generalized Disjunction-Free Generators
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Jumping emerging patterns with negation in transaction databases - Classification and discovery
Information Sciences: an International Journal
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
Transactions on rough sets XII
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
Extraction of association rules based on literalsets
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
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The discovery of frequent patterns has attracted a lot of attention in the data mining community. While an extensive research has been carried out for discovering positive patterns, little has been offered for discovering patterns with negation. An amount of frequent patterns with negation is usually huge and exceeds the number of frequent positive patterns by orders of magnitude. The problem can be significantly alleviated by applying the generalized disjunction-free literal sets representation, which is a concise lossless representation of all frequent patterns, both with and without negation. In this paper, we offer new efficient algorithm GDFLR-SO-Apriori for discovering this representation and evaluate it against the GDFLR-Apriori algorithm.