Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 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
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
A new framework for itemset generation
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Knowledge Discovery in Databases
Knowledge Discovery in Databases
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
IEEE Transactions on Knowledge and Data Engineering
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Mining Surprising Patterns Using Temporal Description Length
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Discovery of temporal patterns from process instances
Computers in Industry - Special issue: Process/workflow mining
A graph model for E-commerce recommender systems
Journal of the American Society for Information Science and Technology
Toward boosting distributed association rule mining by data de-clustering
Information Sciences: an International Journal
A distributed knowledge extraction data mining algorithm
CIS'04 Proceedings of the First international conference on Computational and Information Science
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The large itemset model has been proposed in the literature for finding associations in a large database of sales transactions. A different method for evaluating and finding itemsets referred to as strongly collective itemsets is proposed. We propose a criterion stressing the importance of the actual correlation of the items with one another rather than their absolute level of presence. Previous techniques for finding correlated itemsets are not necessarily applicable to very large databases. We provide an algorithm which provides very good computational efficiency, while maintaining statistical robustness. The fact that this algorithm relies on relative measures rather than absolute measures such as support also implies that the method can be applied to find association rules in data sets in which items may appear in a sizeable percentage of the transactions (dense data sets), data sets in which the items have varying density, or even negative association rules.