Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Extracting Share Frequent Itemsets with Infrequent Subsets
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
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
Weighted Association Rule Mining using weighted support and significance framework
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Weighted Association Rules without Preassigned Weights
IEEE Transactions on Knowledge and Data Engineering
Redundant association rules reduction techniques
International Journal of Business Intelligence and Data Mining
Incorporating pageview weight into an association-rule-based web recommendation system
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
WeightTransmitter: weighted association rule mining using landmark weights
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
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Association Rule Mining is an important data mining technique that has been widely used as an automatic rule generation method. While having outstanding success in many different application domains, it also has the potential to generate a vast number of rules, many of which are of little interest to the user. Weighted Association Rule Mining (WARM) overcomes this problem by assigning weights to items thus enabling interesting rules to be ranked ahead of less interesting ones and making it easier for the user to determine which rules are the most useful. Past research on WARM assumes that users have the necessary knowledge to supply item weights. In this research we relax this assumption by deriving item weights based on interactions between items. Our experimentation shows that the rule bases produced by our scheme produces more compact rule bases with a higher information content than standard rule generation methods.