Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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 and their representations has attracted a lot of attention in the data mining community. An extensive research has been carried out mainly in discovering positive patterns. Recently, the generalized disjunction–free representation GDFLR of all frequent patterns both with and without negation has been proposed. There are cases, however, when a user is interested in patterns with a restricted number of negated items. In this paper, we offer the k-GDFLR representation as an adaptation of GDFLR, which represents all frequent patterns with at most k negated items. Algorithms discovering this representation are discussed as well. The experimental results show that k-GDFLR is more concise than GDFLR.