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KDD
2008
ACM
172views Data Mining» more  KDD 2008»
16 years 6 months ago
Structured metric learning for high dimensional problems
The success of popular algorithms such as k-means clustering or nearest neighbor searches depend on the assumption that the underlying distance functions reflect domain-specific n...
Jason V. Davis, Inderjit S. Dhillon
KDD
2007
ACM
231views Data Mining» more  KDD 2007»
16 years 6 months ago
Xproj: a framework for projected structural clustering of xml documents
XML has become a popular method of data representation both on the web and in databases in recent years. One of the reasons for the popularity of XML has been its ability to encod...
Charu C. Aggarwal, Na Ta, Jianyong Wang, Jianhua F...
KDD
2007
ACM
191views Data Mining» more  KDD 2007»
16 years 6 months ago
Modeling relationships at multiple scales to improve accuracy of large recommender systems
The collaborative filtering approach to recommender systems predicts user preferences for products or services by learning past useritem relationships. In this work, we propose no...
Robert M. Bell, Yehuda Koren, Chris Volinsky
KDD
2007
ACM
191views Data Mining» more  KDD 2007»
16 years 6 months ago
Cost-effective outbreak detection in networks
Given a water distribution network, where should we place sensors to quickly detect contaminants? Or, which blogs should we read to avoid missing important stories? These seemingl...
Andreas Krause, Carlos Guestrin, Christos Faloutso...
KDD
2006
ACM
153views Data Mining» more  KDD 2006»
16 years 6 months ago
Spatial scan statistics: approximations and performance study
Spatial scan statistics are used to determine hotspots in spatial data, and are widely used in epidemiology and biosurveillance. In recent years, there has been much effort invest...
Deepak Agarwal, Andrew McGregor, Jeff M. Phillips,...
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