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» A Weighted Utility Framework for Mining Association Rules
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EMS
2008
IEEE
13 years 11 months ago
A Weighted Utility Framework for Mining Association Rules
Association rule mining (ARM) identifies frequent itemsets from databases and generates association rules by assuming that all items have the same significance and frequency of oc...
M. Sulaiman Khan, Maybin K. Muyeba, Frans Coenen
KDD
2003
ACM
175views Data Mining» more  KDD 2003»
14 years 5 months ago
Weighted Association Rule Mining using weighted support and significance framework
We address the issues of discovering significant binary relationships in transaction datasets in a weighted setting. Traditional model of association rule mining is adapted to han...
Feng Tao, Fionn Murtagh, Mohsen Farid
APIN
2006
168views more  APIN 2006»
13 years 5 months ago
Utilizing Genetic Algorithms to Optimize Membership Functions for Fuzzy Weighted Association Rules Mining
It is not an easy task to know a priori the most appropriate fuzzy sets that cover the domains of quantitative attributes for fuzzy association rules mining. In general, it is unre...
Mehmet Kaya, Reda Alhajj
ADC
2003
Springer
118views Database» more  ADC 2003»
13 years 10 months ago
CrystalBall : A Framework for Mining Variants of Association Rules
The mining of informative rules calls for methods that include different attributes (e.g., weights, quantities, multipleconcepts) suitable for the context of the problem to be an...
Kok-Leong Ong, Wee Keong Ng, Ee-Peng Lim
ICDE
2006
IEEE
130views Database» more  ICDE 2006»
14 years 6 months ago
MIC Framework: An Information-Theoretic Approach to Quantitative Association Rule Mining
We propose a framework, called MIC, which adopts an information-theoretic approach to address the problem of quantitative association rule mining. In our MIC framework, we first d...
Yiping Ke, James Cheng, Wilfred Ng