Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 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
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
A new framework for itemset generation
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Transversing itemset lattices with statistical metric pruning
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Turbo-charging vertical mining of large databases
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
Efficient search for association rules
Proceedings of the sixth 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
Data Organization and Access for Efficient Data Mining
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Mining Significant Pairs of Patterns from Graph Structures with Class Labels
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining top-k strongly correlated item pairs without minimum correlation threshold
International Journal of Knowledge-based and Intelligent Engineering Systems
Correlated itemset mining in ROC space: a constraint programming approach
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A log-linear approach to mining significant graph-relational patterns
Data & Knowledge Engineering
Itemset mining: A constraint programming perspective
Artificial Intelligence
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Many algorithms have been proposed for computing association rules using the support-confidence framework. One drawback of this framework is its weakness in expressing the notion of correlation. We propose an efficient algorithm for mining association rules that uses statistical metrics to determine correlation. The simple application of conventional techniques developed for the support-confidence framework is not possible, since functions for correlation do not meet the anti-monotonicity property that is crucial to traditional methods. In this paper, we propose the heuristics for the vertical decomposition of a database, for pruning unproductive itemsets, and for traversing a set-enumeration tree of itemsets that is tailored to the calculation of the N most significant association rules, where N can be specified by the user. We experimentally compared the combination of these three techniques with the previous statistical approach. Our tests confirmed that the comutational performance improves by several orders of magnitude.