Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 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
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Both Positive and Negative Association Rules
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Mining for Strong Negative Associations in a Large Database of Customer Transactions
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Mining Negative Association Rules
ISCC '02 Proceedings of the Seventh International Symposium on Computers and Communications (ISCC'02)
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
Mining Infrequent Itemsets Based on Multiple Level Minimum Supports
ICICIC '07 Proceedings of the Second International Conference on Innovative Computing, Informatio and Control
Elicitation of fuzzy association rules from positive and negative examples
Fuzzy Sets and Systems
Extended negative association rules and the corresponding mining algorithm
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Study of positive and negative association rules based on multi-confidence and chi-squared test
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
A novel approach of multilevel positive and negative association rule mining for spatial databases
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Mining interesting infrequent and frequent itemsets based on minimum correlation strength
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Minimally infrequent itemset mining using pattern-growth paradigm and residual trees
Proceedings of the 17th International Conference on Management of Data
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A lot of new problems may occur when we simultaneously study positive and negative association rules(PNARs), i.e., the forms A$\Rightarrow$ B, A$\Rightarrow\neg$ B, ¬A$\Rightarrow$ Band ¬A$\Rightarrow\neg$ B. These problems include how to discover infrequent itemsets, how to generate PNARs correctly, how to solve the problem caused by a single minimum support and so on. Infrequent itemsets become very important because there are many valued negative association rules (NARs) in them. In our previous work, a MLMS model was proposed to discover simultaneously both frequent and infrequent itemsets by using multiple level minimum supports (MLMS) model. In this paper, a new measure VARCCwhich combines correlation coefficient and minimum confidence is proposed and a corresponding algorithm PNAR_MLMSis also proposed to generate PNARs correctly from the frequent and infrequent itemsets discovered by the MLMS model. The experimental results show that the measure and the algorithm are effective.