DocumentCode
2724108
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
fYear
2007
fDate
March 1 2007-April 5 2007
Firstpage
26
Lastpage
30
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence and Data Mining, 2007. CIDM 2007. IEEE Symposium on
Conference_Location
Honolulu, HI
Print_ISBN
1-4244-0705-2
Type
conf
DOI
10.1109/CIDM.2007.368848
Filename
4221272
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