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» The hardness and approximation algorithms for l-diversity
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STOC
2005
ACM
129views Algorithms» more  STOC 2005»
14 years 7 months ago
Learning with attribute costs
We study an extension of the "standard" learning models to settings where observing the value of an attribute has an associated cost (which might be different for differ...
Haim Kaplan, Eyal Kushilevitz, Yishay Mansour
KDD
2008
ACM
174views Data Mining» more  KDD 2008»
14 years 7 months ago
Effective label acquisition for collective classification
Information diffusion, viral marketing, and collective classification all attempt to model and exploit the relationships in a network to make inferences about the labels of nodes....
Mustafa Bilgic, Lise Getoor
KDD
2007
ACM
159views Data Mining» more  KDD 2007»
14 years 7 months ago
Constraint-driven clustering
Clustering methods can be either data-driven or need-driven. Data-driven methods intend to discover the true structure of the underlying data while need-driven methods aims at org...
Rong Ge, Martin Ester, Wen Jin, Ian Davidson
SIGMOD
2008
ACM
164views Database» more  SIGMOD 2008»
14 years 7 months ago
Finding frequent items in probabilistic data
Computing statistical information on probabilistic data has attracted a lot of attention recently, as the data generated from a wide range of data sources are inherently fuzzy or ...
Qin Zhang, Feifei Li, Ke Yi
GECCO
2009
Springer
138views Optimization» more  GECCO 2009»
14 years 1 months ago
IMAD: in-execution malware analysis and detection
The sophistication of computer malware is becoming a serious threat to the information technology infrastructure, which is the backbone of modern e-commerce systems. We, therefore...
Syed Bilal Mehdi, Ajay Kumar Tanwani, Muddassar Fa...