Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 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
A new framework for itemset generation
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
ACM Computing Surveys (CSUR)
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Alternative Interest Measures for Mining Associations in Databases
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Database classification for multi-database mining
Information Systems
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Mining compressed frequent-pattern sets
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Efficient clustering of databases induced by local patterns
Decision Support Systems
Clustering local frequency items in multiple databases
Information Sciences: an International Journal
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Measuring association among variables is an important step for finding solutions to many data mining problems. An existing metric might not be effective to serve as a measure of association among a set of items in a database. In this paper, we propose two measures of association, A"1 and A"2. We introduce the notion of associative itemset in a database. We express the proposed measures in terms of supports of itemsets. In addition, we provide theoretical foundations of our work. We present experimental results on both real and synthetic databases to show the effectiveness of A"2.