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KDD
2003
ACM
160views Data Mining» more  KDD 2003»
15 years 10 months ago
Playing hide-and-seek with correlations
We present a method for very high-dimensional correlation analysis. The method relies equally on rigorous search strategies and on human interaction. At each step, the method cons...
Chris Jermaine
KDD
2003
ACM
162views Data Mining» more  KDD 2003»
15 years 10 months ago
Improving spatial locality of programs via data mining
In most computer systems, page fault rate is currently minimized by generic page replacement algorithms which try to model the temporal locality inherent in programs. In this pape...
Karlton Sequeira, Mohammed Javeed Zaki, Boleslaw K...
KDD
2003
ACM
175views Data Mining» more  KDD 2003»
15 years 10 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
2002
ACM
171views Data Mining» more  KDD 2002»
15 years 10 months ago
Mining complex models from arbitrarily large databases in constant time
In this paper we propose a scaling-up method that is applicable to essentially any induction algorithm based on discrete search. The result of applying the method to an algorithm ...
Geoff Hulten, Pedro Domingos
KDD
2001
ACM
216views Data Mining» more  KDD 2001»
15 years 10 months ago
Real world performance of association rule algorithms
This study compares five well-known association rule algorithms using three real-world datasets and an artificial dataset. The experimental results confirm the performance improve...
Zijian Zheng, Ron Kohavi, Llew Mason