Abstract
Frequent itemset mining is a common task in data mining from which association rules are derived. As the frequent itemsets can be considered as a kind of summary of the original databases, recently the inverse frequent set mining problem has received more attention because of its potential threat to the privacy of the original dataset. Since this inverse problem has been proven to be NP-complete, people ask “Are there reasonably efficient search strategies to find a compatible data set in practice?” [1]. This paper describes our effort towards finding a feasible solution to address this problem.
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References
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© 2005 Springer-Verlag Berlin Heidelberg
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Chen, X., Orlowska, M. (2005). A Further Study on Inverse Frequent Set Mining. In: Li, X., Wang, S., Dong, Z.Y. (eds) Advanced Data Mining and Applications. ADMA 2005. Lecture Notes in Computer Science(), vol 3584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527503_89
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DOI: https://doi.org/10.1007/11527503_89
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27894-8
Online ISBN: 978-3-540-31877-4
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