Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
Mining association rules on significant rare data using relative support
Journal of Systems and Software
Weighted Association Rule Mining using weighted support and significance framework
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
WAR: Weighted Association Rules for Item Intensities
Knowledge and Information Systems
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Efficient mining of weighted interesting patterns with a strong weight and/or support affinity
Information Sciences: an International Journal
Mining fuzzy association rules in a bank-account database
IEEE Transactions on Fuzzy Systems
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Mining weighted association rules are very important in a domain of knowledge discovery. Most of traditional association rules are focused on binary relationships rather than the mixture binary-weight relationships of items. As a result, the significance of weight of each item in a transaction is just ignored completely. Until this instance, only few studies are dedicated for weighted schemes as compared to existing binary association rules. Here, we propose a novel weighted association rules scheme called Relative Weighted Support (RWS). The result reveals that RWS can easily discover the significance of weighted association rules and surprisingly all of them are extracted from the least items.