Using association rules for product assortment decisions: a case study
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient discovery of error-tolerant frequent itemsets in high dimensions
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Approximate Frequent Itemsets from Noisy Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Mining Maximal Quasi-Bicliques to Co-Cluster Stocks and Financial Ratios for Value Investment
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Quantitative evaluation of approximate frequent pattern mining algorithms
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Statistical Information of Frequent Fault-Tolerant Patterns in Transactional Databases
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Constraint-Based mining of fault-tolerant patterns from boolean data
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
CloseViz: visualizing useful patterns
Proceedings of the ACM SIGKDD Workshop on Useful Patterns
MFCluster: mining maximal fault-tolerant constant row biclusters in microarray dataset
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Fast mining erasable itemsets using NC_sets
Expert Systems with Applications: An International Journal
Substructure clustering: a novel mining paradigm for arbitrary data types
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
A knowledge-driven bi-clustering method for mining noisy datasets
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Closed and noise-tolerant patterns in n-ary relations
Data Mining and Knowledge Discovery
MEI: An efficient algorithm for mining erasable itemsets
Engineering Applications of Artificial Intelligence
Interesting pattern mining in multi-relational data
Data Mining and Knowledge Discovery
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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 www.cais.ntu.edu.sg/~vivek/pubs/ftfim09.