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» Evaluation of Sampling for Data Mining of Association Rules
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FIMI
2004
175views Data Mining» more  FIMI 2004»
15 years 2 months ago
CT-PRO: A Bottom-Up Non Recursive Frequent Itemset Mining Algorithm Using Compressed FP-Tree Data Structure
Frequent itemset mining (FIM) is an essential part of association rules mining. Its application for other data mining tasks has also been recognized. It has been an active researc...
Yudho Giri Sucahyo, Raj P. Gopalan
85
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FLAIRS
2004
15 years 2 months ago
Personalization Using Hybrid Data Mining Approaches in E-Business Applications
Effective personalization is greatly demanded in highly heterogeneous and diverse e-commerce domain. In our approach we rely on the idea that an effective personalization techniqu...
Olena Parkhomenko, Yugyung Lee, Chintan Patel
143
Voted
IJAR
2011
118views more  IJAR 2011»
14 years 4 months ago
A sequential pattern mining algorithm using rough set theory
Sequential pattern mining is a crucial but challenging task in many applications, e.g., analyzing the behaviors of data in transactions and discovering frequent patterns in time se...
Ken Kaneiwa, Yasuo Kudo
85
Voted
ICSE
2008
IEEE-ACM
16 years 22 days ago
Formal concept analysis applied to fault localization
One time-consuming task in the development of software is debugging. Recent work in fault localization crosschecks traces of correct and failing execution traces, it implicitly se...
Peggy Cellier
113
Voted
KDD
2008
ACM
138views Data Mining» more  KDD 2008»
16 years 1 months ago
Quantitative evaluation of approximate frequent pattern mining algorithms
Traditional association mining algorithms use a strict definition of support that requires every item in a frequent itemset to occur in each supporting transaction. In real-life d...
Rohit Gupta, Gang Fang, Blayne Field, Michael Stei...