Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 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
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining for association rules and sequential patterns: sequential and parallel algorithms
Data mining for association rules and sequential patterns: sequential and parallel algorithms
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
An efficient algorithm for mining closed inter-transaction itemsets
Data & Knowledge Engineering
Mining frequent closed patterns in pointset databases
Information Systems
Mining minimal non-redundant association rules using frequent itemsets lattice
International Journal of Intelligent Systems Technologies and Applications
DBV-Miner: A Dynamic Bit-Vector approach for fast mining frequent closed itemsets
Expert Systems with Applications: An International Journal
A lattice-based approach for mining most generalization association rules
Knowledge-Based Systems
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Complete set of itemsets can be grouped into non-overlapping clusters identified by closed tidsets. Each cluster has only one closed itemset and is the superset of all itemsets with the same support. Number of closed itemsets is identical to the number of clusters. Therefore, the problem of discovering closed itemsets can be considered as the problem of clustering the complete set of itemsets by closed tidsets. In this paper, we present CloseMiner, a new algorithm for discovering all frequent closed itemsets by grouping the complete set of itemsets into non-overlapping clusters identified by closed tidsets. An extensive experimental evaluation on a number of real and synthetic databases shows that CloseMiner outperforms Apriori and CHARM.