Title :
Extracting Borderline Associations
Author :
Chen, Wei Kian ; Baumgartner, Dustin ; Millikin, Ryan
Author_Institution :
Dept. of Electr. & Comput. Eng. & Comput. Sci., Ohio Northern Univ., Ada, OH
fDate :
March 1 2007-April 5 2007
Abstract :
In this paper, we present an extension of the well known algorithm for association mining, Apriori. This extended algorithm, ApriorBL, considers associations between items which occur together - focusing solely on the borderline cases. These borderline cases occur often enough to provide valuable information; however, there are currently no algorithms that target them. We discuss how the AprioriBL algorithm works and present a comparative analysis of Apriori and AprioriBL
Keywords :
data mining; Apriori association mining; AprioriBL algorithm; borderline association extraction; Algorithm design and analysis; Computational intelligence; Computer science; Data engineering; Data mining; Educational institutions; Frequency; Itemsets; Terminology; Testing;
Conference_Titel :
Computational Intelligence and Data Mining, 2007. CIDM 2007. IEEE Symposium on
Conference_Location :
Honolulu, HI
Print_ISBN :
1-4244-0705-2
DOI :
10.1109/CIDM.2007.368848
