Using association rules for product assortment decisions: a case study
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient mining of weighted association rules (WAR)
Proceedings of the sixth 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
Theoretical Comparison between the Gini Index and Information Gain Criteria
Annals of Mathematics and Artificial Intelligence
Mining Weighted Association Rules without Preassigned Weights
IEEE Transactions on Knowledge and Data Engineering
EWgen: automatic generation of item weights for weighted association rule mining
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
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
Valency based weighted association rule mining
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Rule-Based Semantic Concept Classification from Large-Scale Video Collections
International Journal of Multimedia Data Engineering & Management
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Weighted Association Rule Mining (WARM) is a technique that is commonly used to overcome the well-known limitations of the classical Association Rule Mining approach. The assignment of high weights to important items enables rules that express relationships between high weight items to be ranked ahead of rules that only feature less important items. Most previous research to weight assignment has used subjective measures to assign weights and are reliant on domain specific information. Whilst there have been a few approaches that automatically deduce weights from patterns of interaction between items, none of them take advantage of the situation where weights of only a subset of items are known in advance. We propose a model, WeightTransmitter, that interpolates the unknown weights from a known subset of weights.