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» Combined association rules for dealing with missing values
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DASFAA
2007
IEEE
234views Database» more  DASFAA 2007»
14 years 1 months ago
Estimating Missing Data in Data Streams
Networks of thousands of sensors present a feasible and economic solution to some of our most challenging problems, such as real-time traffic modeling, military sensing and trackin...
Nan Jiang, Le Gruenwald
KAIS
2008
119views more  KAIS 2008»
13 years 7 months ago
An information-theoretic approach to quantitative association rule mining
Abstract. Quantitative Association Rule (QAR) mining has been recognized an influential research problem over the last decade due to the popularity of quantitative databases and th...
Yiping Ke, James Cheng, Wilfred Ng
ESEM
2008
ACM
13 years 9 months ago
A hybrid faulty module prediction using association rule mining and logistic regression analysis
This paper proposes a fault-prone module prediction method that combines association rule mining with logistic regression analysis. In the proposed method, we focus on three key m...
Yasutaka Kamei, Akito Monden, Shuuji Morisaki, Ken...
JCST
2008
119views more  JCST 2008»
13 years 7 months ago
Mining Frequent Generalized Itemsets and Generalized Association Rules Without Redundancy
This paper presents some new algorithms to efficiently mine max frequent generalized itemsets (g-itemsets) and essential generalized association rules (g-rules). These are compact ...
Daniel Kunkle, Donghui Zhang, Gene Cooperman
ICDM
2007
IEEE
145views Data Mining» more  ICDM 2007»
14 years 1 months ago
Using Data Mining to Estimate Missing Sensor Data
Estimating missing sensor values is an inherent problem in sensor network applications; however, existing data estimation approaches do not apply well to the context of datastream...
Le Gruenwald, Hamed Chok, Mazen Aboukhamis