Weighted Association Rule Mining using Weighted Support and Significance Framework

Feng Tao, Fionn Murtagh, Mohsen Farid

Research output: Chapter in Book/Report/Conference proceedingConference contribution

263 Citations (Scopus)

Abstract

We address the issues of discovering significant binary relationships in transaction datasets in a weighted setting. Traditional model of association rule mining is adapted to handle weighted association rule mining problems where each item is allowed to have a weight. The goal is to steer the mining focus to those significant relationships involving items with significant weights rather than being flooded in the combinatorial explosion of insignificant relationships. We identify the challenge of using weights in the iterative process of generating large itemsets. The problem of invalidation of the “downward closure property” in the weighted setting is solved by using an improved model of weighted support measurements and exploiting a “weighted downward closure property”. A new algorithm called WARM (Weighted Association Rule Mining) is developed based on the improved model. The algorithm is both scalable and efficient in discovering significant relationships in weighted settings as illustrated by experiments performed on simulated datasets.
Original languageEnglish
Title of host publicationKDD '03
Subtitle of host publicationProceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
PublisherAssociation for Computing Machinery (ACM)
Pages661-666
Number of pages6
ISBN (Print)1581137370, 9781581137378
DOIs
Publication statusPublished - 2003
Externally publishedYes
Event9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Washington, United States
Duration: 24 Aug 200327 Aug 2003
Conference number: 9
https://www.tib.eu/en/search/id/TIBKAT%3A379407477/

Conference

Conference9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Abbreviated titleKDD03
CountryUnited States
CityWashington
Period24/08/0327/08/03
Internet address

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  • Cite this

    Tao, F., Murtagh, F., & Farid, M. (2003). Weighted Association Rule Mining using Weighted Support and Significance Framework. In KDD '03 : Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 661-666). Association for Computing Machinery (ACM). https://doi.org/10.1145/956750.956836