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KDD
2008
ACM
161views Data Mining» more  KDD 2008»
16 years 5 months ago
Locality sensitive hash functions based on concomitant rank order statistics
: Locality Sensitive Hash functions are invaluable tools for approximate near neighbor problems in high dimensional spaces. In this work, we are focused on LSH schemes where the si...
Kave Eshghi, Shyamsundar Rajaram
127
Voted
KDD
2010
ACM
224views Data Mining» more  KDD 2010»
15 years 9 months ago
Ensemble pruning via individual contribution ordering
An ensemble is a set of learned models that make decisions collectively. Although an ensemble is usually more accurate than a single learner, existing ensemble methods often tend ...
Zhenyu Lu, Xindong Wu, Xingquan Zhu, Josh Bongard
WWW
2006
ACM
16 years 5 months ago
Template guided association rule mining from XML documents
Compared with traditional association rule mining in the structured world (e.g. Relational Databases), mining from XML data is confronted with more challenges due to the inherent ...
Rahman Ali Mohammadzadeh, Sadegh Soltan, Masoud Ra...
142
Voted
KDD
1998
ACM
105views Data Mining» more  KDD 1998»
15 years 9 months ago
PlanMine: Sequence Mining for Plan Failures
This paper presents the PLANMINE sequence mining algorithm to extract patterns of events that predict failures in databases of plan executions. New techniques were needed because ...
Mohammed Javeed Zaki, Neal Lesh, Mitsunori Ogihara
AIR
2000
91views more  AIR 2000»
15 years 4 months ago
PlanMine: Predicting Plan Failures Using Sequence Mining
This paper presents the PLANMINE sequence mining algorithm to extract patterns of events that predict failures in databases of plan executions. New techniques were needed because p...
Mohammed Javeed Zaki, Neal Lesh, Mitsunori Ogihara