DocumentCode
2741276
Title
Mining Positive and Negative Association Rules from Large Databases
Author
Cornelis, Chris ; Yan, Peng ; Zhang, Xing ; Chen, Guoqing
Author_Institution
Dept. Appl. Math, & Comput. of Sci., Ghent Univ.
fYear
2006
fDate
7-9 June 2006
Firstpage
1
Lastpage
6
Abstract
This paper is concerned with discovering positive and negative association rules, a problem which has been addressed by various authors from different angles, but for which no fully satisfactory solution has yet been proposed. We catalogue and critically examine the existing definitions and approaches, and we present an a priori-based algorithm that is able to find all valid positive and negative association rules in a support-confidence framework. Efficiency is guaranteed by exploiting an upward closure property that holds for the support of negative association rules under our definition of validity
Keywords
data mining; very large databases; a priori-based algorithm; association rule mining; data mining; large databases; Association rules; Computational efficiency; Data mining; Economic forecasting; Explosives; Heart; Information filtering; Information filters; Itemsets; Transaction databases; Apriori; association rules; data mining;
fLanguage
English
Publisher
ieee
Conference_Titel
Cybernetics and Intelligent Systems, 2006 IEEE Conference on
Conference_Location
Bangkok
Print_ISBN
1-4244-0023-6
Type
conf
DOI
10.1109/ICCIS.2006.252251
Filename
4017810
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