Abstract
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.
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Kryszkiewicz, M., Cichoń, K. (2005). Support Oriented Discovery of Generalized Disjunction-Free Representation of Frequent Patterns with Negation. In: Ho, T.B., Cheung, D., Liu, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2005. Lecture Notes in Computer Science(), vol 3518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11430919_77
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DOI: https://doi.org/10.1007/11430919_77
Publisher Name: Springer, Berlin, Heidelberg
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