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
Identifying non-actionable association rules
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
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
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
Efficient mining of both positive and negative association rules
ACM Transactions on Information Systems (TOIS)
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
Mathematical and Computer Modelling: An International Journal
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Using a single confidence threshold will result in a dilemmatic situation when simultaneously studying positive and negative association rule (PNAR), i.e., the forms A$\Rightarrow$B, A$\Rightarrow$¬B, ¬A$\Rightarrow$B and ¬A$\Rightarrow$¬B. A method based on four confidence thresholds for the four forms of PNARs is proposed. The relationships among the four confidences, which show the necessity of using multiple confidence thresholds, are also discussed. In addition, the chi-squared test can avoid generating misleading rules that maybe occur when simultaneously studying the PNARs. The method of how to apply chi-squared test in mining association rules is discussed. An algorithm PNARMC based on the chi-squared test and the four confidence thresholds is proposed. The experimental results demonstrate that the algorithm can not only generate PNARs rightly, but can also control the total number of rules flexibly.