Efficient association rule mining among both frequent and infrequent items

Authors:
Ling Zhou;Stephen Yau
Affiliations:
Department of Mathematics, Statistics and Computer Science, University of Illinois at Chicago, IL, United States;Department of Mathematics, Statistics and Computer Science, University of Illinois at Chicago, IL, United States
Venue:
Computers & Mathematics with Applications
Year:
2007

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Abstract

Association rule mining among frequent items has been extensively studied in data mining research. However, in recent years, there has been an increasing demand for 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.