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Fast Borderline Mining

Year: 
2007
Researcher(s): 
Dustin Baumgartner, Ryan Millikin
Institution: 
Ohio Northern University
Discipline: 
Computer Science

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.

ONU Student Research Colloquium