Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Quantifiable data mining using principal component analysis
Quantifiable data mining using principal component analysis
Mining association rules with multiple minimum supports
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
Finding Interesting Associations without Support Pruning
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A two-dimensional interpolation function for irregularly-spaced data
ACM '68 Proceedings of the 1968 23rd ACM national conference
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
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Mining Weighted Association Rules without Preassigned Weights
IEEE Transactions on Knowledge and Data Engineering
Finding sporadic rules using apriori-inverse
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery 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
Automatic assignment of item weights for pattern mining on data streams
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Discriminatory confidence analysis in pattern mining
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
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
Weighted association rule mining via a graph based connectivity model
Information Sciences: an International Journal
Automatic Item Weight Generation for Pattern Mining and its Application
International Journal of Data Warehousing and Mining
ICDM'13 Proceedings of the 13th international conference on Advances in Data Mining: applications and theoretical aspects
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Association rule mining is an important data mining task that discovers relationships among items in a transaction database. Most approaches to association rule mining assume that all items within a dataset have a uniform distribution with respect to support. Therefore, weighted association rule mining (WARM) was introduced to provide a notion of importance to individual items. Previous approaches to the weighted association rule mining problem require users to assign weights to items. This is infeasible when millions of items are present in a dataset. In this paper we propose a method that is based on a novel Valency model that automatically infers item weights based on interactions between items. Our experimentation shows that the weighting scheme results in rules that better capture the natural variation that occurs in a dataset when compared to a miner that does not employ such a weighting scheme.