Abstract
Recently, negative association rule mining has received some attention and proved to be useful. This paper proposes an extended form for negative association rules and defines extended negative association rules. Furthermore, a corresponding algorithm is devised for mining extended negative association rules. The extended form is more general and expressive than the three existing forms. The proposed mining algorithm overcomes some limitations of previous mining methods, and experimental results show that it is efficient on simple and sparse datasets when minimum support is high to some degree. Our work will extend related applications of negative association rules to a broader range.
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Gan, M., Zhang, M., Wang, S. (2006). Extended Negative Association Rules and the Corresponding Mining Algorithm. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_17
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DOI: https://doi.org/10.1007/11739685_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33584-9
Online ISBN: 978-3-540-33585-6
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