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» Identifying non-actionable 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
2002
ACM
214views Data Mining» more  KDD 2002»
14 years 5 months ago
Privacy preserving association rule mining in vertically partitioned data
Privacy considerations often constrain data mining projects. This paper addresses the problem of association rule mining where transactions are distributed across sources. Each si...
Jaideep Vaidya, Chris Clifton
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
KDD
2004
ACM
125views Data Mining» more  KDD 2004»
14 years 5 months ago
Differential Association Rule Mining for the Study of Protein-Protein Interaction Networks
Protein-protein interactions are of great interest to biologists. A variety of high-throughput techniques have been devised, each of which leads to a separate definition of an int...
Christopher Besemann, Anne Denton, Ajay Yekkirala,...
PAKDD
2005
ACM
94views Data Mining» more  PAKDD 2005»
13 years 10 months ago
Progressive Sampling for Association Rules Based on Sampling Error Estimation
We explore in this paper a progressive sampling algorithm, called Sampling Error Estimation (SEE), which aims to identify an appropriate sample size for mining association rules. S...
Kun-Ta Chuang, Ming-Syan Chen, Wen-Chieh Yang