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
Online association rule mining
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Pincer Search: A New Algorithm for Discovering the Maximum Frequent Set
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Data Mining Techniques for Associations, Clustering and Classification
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
On Objective Measures of Rule Surprisingness
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Deciding monotone duality and identifying frequent itemsets in quadratic logspace
Proceedings of the 32nd symposium on Principles of database systems
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Any algorithm for mining association rules must discover the set of all maximal frequent itemsets (maxL) from a database. Given a set of itemsets X, to verify that X is maxL, two conditions must be checked: (1) any itemset x in X is frequent, and (2) the dual of X must be the set of all minimal infrequent itemsets (minS). This observation leads us to a family of algorithms for mining association rules. Given a reasonable guess of minS and maxL, we verify their duality relationship, and refine the two sets until the above two conditions hold. We note that previously proposed algorithms such as Apriori and Pincer-Search are all members of our algorithm family. Also, we study a member algorithm called FlipFlop. Through a series of experiments, we show that FlipFlop significantly reduces the I/O requirement of mining association rules.