Probabilistic logic programming with conditional constraints
ACM Transactions on Computational Logic (TOCL)
Feasible itemset distributions in data mining: theory and application
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Computational complexity of itemset frequency satisfiability
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Itemset frequency satisfiability: Complexity and axiomatization
Theoretical Computer Science
An audit environment for outsourcing of frequent itemset mining
Proceedings of the VLDB Endowment
A FP-tree-based method for inverse frequent set mining
BNCOD'06 Proceedings of the 23rd British National Conference on Databases, conference on Flexible and Efficient Information Handling
Bands of privacy preserving objectives: classification of PPDM strategies
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
Solving inverse frequent itemset mining with infrequency constraints via large-scale linear programs
ACM Transactions on Knowledge Discovery from Data (TKDD)
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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.