Mining association rules between sets of items in large databases
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Efficient mining of association rules using closed itemset lattices
Information Systems
Mining frequent patterns without candidate generation
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Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
A condensed representation to find frequent patterns
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
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ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
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Reliable representations for association rules
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PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A survey on condensed representations for frequent sets
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Key roles of closed sets and minimal generators in concise representations of frequent patterns
Intelligent Data Analysis
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Given a large set of data, a common data mining problem is to extract the frequent patterns occurring in this set. The idea presented in this paper is to extract a condensed representation of the frequent patterns called disjunction-bordered condensation (DBC), instead of extracting the whole frequent pattern collection. We show that this condensed representation can be used to regenerate all frequent patterns and their exact frequencies. Moreover, this regeneration can be performed without any access to the original data. Practical experiments show that the DBCcan be extracted very efficiently even in difficult cases and that this extraction and the regeneration of the frequent patterns is much more efficient than the direct extraction of the frequent patterns themselves. We compared the DBC with another representation of frequent patterns previously investigated in the literature called frequent closed sets. In nearly all experiments we have run, the DBC have been extracted much more efficiently than frequent closed sets. In the other cases, the extraction times are very close.