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Towards Efficient Mining of Proportional Fault-Tolerant Frequent Itemsets

Towards Efficient Mining of Proportional Fault-Tolerant Frequent Itemsets

This video was recorded at 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Paris 2009. Fault-tolerant frequent itemsets (FTFI) are variants of frequent itemsets for representing and discovering generalized knowledge. However, despite growing interest in this field, no previous approach mines proportional FTFIs with their exact support (FT-support). This problem is difficult because of two concerns: (a) non anti-monotonic property of FT-support when relaxation is proportional, and (b) difficulty in computing FT-support. Previous efforts on this problem either simplify the general problem by adding constraints, or provide approximate solutions without any error guarantees. In this paper, we address these concerns in the general FTFI mining problem. We limit the search space by providing provably correct anti monotone bounds for FT-support and develop practically efficient means of achieving them. Besides, we also provide an efficient and exact FT-support counting procedure. Extensive experiments using real datasets validate that our solution is reasonably efficient for completely mining FTFIs. Implementations for the algorithms are available from


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