Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient discovery of error-tolerant frequent itemsets in high dimensions
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining frequent item sets by opportunistic projection
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining uncertain data for frequent itemsets that satisfy aggregate constraints
Proceedings of the 2010 ACM Symposium on Applied Computing
Frequent itemset mining of uncertain data streams using the damped window model
Proceedings of the 2011 ACM Symposium on Applied Computing
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The mining of frequent patterns in databases has been studied for several years. However, the real-world data tends to be dirty and frequent pattern mining which extracts patterns that are absolutely matched is not enough. An approach, called fault-tolerant frequent pattern (FT-pattern) mining, is more suitable for extracting interesting information from real-world data that may be polluted by noise. In our approach, the problems of mining proportional and fixed FT-patterns are considered. In proportional FT-pattern mining, the number of faults tolerable in a pattern is proportional to the length of the pattern. And the number of faults tolerable in different length of patterns is fixed in fixed FT-pattern mining. A new graph structure, FT-association graph, is proposed to help us filtering out impossible candidates with high efficiency. The experimental results show that the proposed algorithms of our approach are highly efficient for mining both proportional and fixed FT-patterns.