Fast borderline Association Mining

Authors:
Wei Kian Chen;Dustin Baumgartner;Ryan Millikin
Affiliations:
Ohio Northern University, Ada, Ohio;University of Toledo, Toledo, Ohio;Astronautics Corporation of America, Milwaukee, Wisconsin
Venue:
ACST '08 Proceedings of the Fourth IASTED International Conference on Advances in Computer Science and Technology
Year:
2008

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Abstract

In this paper, we present a modification to the AprioriBL algorithm, which is an extension to a well-known Association Mining algorithm, Apriori. AprioriBL targets the borderline cases of frequent itemsets; however, it performs poorly. Our new algorithm, AprioriBLT, considers only the borderline cases for generating itemsets. This increases performance at the cost of accuracy. A comparison is made between AprioriBL and AprioriBLT, and the efficacy of AprioriBLT is discussed.