Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Computational complexity of itemset frequency satisfiability
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Approximate Inverse Frequent Itemset Mining: Privacy, Complexity, and Approximation
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
A further study on inverse frequent set mining
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Efficient Mining of Frequent Itemsets from Data Streams
BNCOD '08 Proceedings of the 25th British national conference on Databases: Sharing Data, Information and Knowledge
WSFI-Mine: Mining Frequent Patterns in Data Streams
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
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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Recently, the inverse frequent set mining problem has received more attention because of its important applications in different privacy preserving data sharing contexts. Several studies were reported to probe the NP-complete problem of inverse frequent set mining. However, it is still an open problem that whether there are reasonably efficient search strategies to find a compatible data set in practice. In this paper, we propose a FP-tree-based method for the inverse problem. Compared with previous “generation-and-test” methods, our method is a zero trace back algorithm, which saves huge computational costs. Furthermore, our algorithm provides a good heuristic search strategy to rapidly find a FP-tree, leading to rapidly finding the compatible databases. More importantly, our method can find a set of compatible databases instead of finding only one compatible database in previous methods.