Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Applying Objective Interestingness Measures in Data Mining Systems
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Selecting the right objective measure for association analysis
Information Systems - Knowledge discovery and data mining (KDD 2002)
Efficient mining of both positive and negative association rules
ACM Transactions on Information Systems (TOIS)
Mining positive and negative association rules: an approach for confined rules
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Mining Infrequent Itemsets Based on Multiple Level Minimum Supports
ICICIC '07 Proceedings of the Second International Conference on Innovative Computing, Informatio and Control
Mining Both Positive and Negative Association Rules from Frequent and Infrequent Itemsets
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Mining Interesting Infrequent and Frequent Itemsets Based on MLMS Model
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
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IMLMS (interesting MLMS (Multiple Level Minimum Supports)) model, which was proposed in our previous works, is designed for pruning uninteresting infrequent and frequent itemsets discovered by MLMS model. One of the pruning measures used in IMLMS model, interest, can be described as follows: to two disjoint itemsets A,B, if interest(A,B)=|s(A∪B) - s(A)s(B)|