Association rule and quantitative association rule mining among infrequent items

Authors:
Ling Zhou;Stephen Yau
Affiliations:
University of Illinois at Chicago;University of Illinois at Chicago
Venue:
Proceedings of the 8th international workshop on Multimedia data mining: (associated with the ACM SIGKDD 2007)
Year:
2007

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Abstract

Association rule mining among frequent items has been extensively studied in data mining research. However, in the recent years, there is an increasing demand of mining the infrequent items (such as rare but expensive items). Since exploring interesting relationship among infrequent items has not been discussed much in the literature, in this paper, we propose two simple, practical and effective schemes to mine association rules among rare items. Our algorithm can also be applied to frequent items with bounded length. Experiments are performed on the well-known IBM synthetic database. Our schemes compare favorably to Apriori and FP-growth under the situation being evaluated. In addition, we explore quantitative association rule mining in transactional database among infrequent items by associating quantities of items purchased; some interesting examples are drawn to illustrate the significance of such mining.