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» Learning from Logged Implicit Exploration Data
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AI
2006
Springer
14 years 6 days ago
Adaptive Fraud Detection Using Benford's Law
Abstract. Adaptive Benford's Law [1] is a digital analysis technique that specifies the probabilistic distribution of digits for many commonly occurring phenomena, even for in...
Fletcher Lu, J. Efrim Boritz, H. Dominic Covvey
ML
2008
ACM
13 years 8 months ago
Discovering significant patterns
Pattern discovery techniques, such as association rule discovery, explore large search spaces of potential patterns to find those that satisfy some user-specified constraints. Due...
Geoffrey I. Webb
AIR
2004
113views more  AIR 2004»
13 years 8 months ago
Class Noise vs. Attribute Noise: A Quantitative Study
Real-world data is never perfect and can often suffer from corruptions (noise) that may impact interpretations of the data, models created from the data and decisions made based on...
Xingquan Zhu, Xindong Wu
KDD
2005
ACM
139views Data Mining» more  KDD 2005»
14 years 8 months ago
Reasoning about sets using redescription mining
Redescription mining is a newly introduced data mining problem that seeks to find subsets of data that afford multiple definitions. It can be viewed as a generalization of associa...
Mohammed Javeed Zaki, Naren Ramakrishnan
ICDM
2009
IEEE
109views Data Mining» more  ICDM 2009»
14 years 3 months ago
Semi-naive Exploitation of One-Dependence Estimators
—It is well known that the key of Bayesian classifier learning is to balance the two important issues, that is, the exploration of attribute dependencies in high orders for ensu...
Nan Li, Yang Yu, Zhi-Hua Zhou