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» Evaluating algorithms that learn from data streams
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ICML
2006
IEEE
16 years 7 months ago
A new approach to data driven clustering
We consider the problem of clustering in its most basic form where only a local metric on the data space is given. No parametric statistical model is assumed, and the number of cl...
Arik Azran, Zoubin Ghahramani
JMLR
2010
134views more  JMLR 2010»
15 years 1 months ago
Bayesian Algorithms for Causal Data Mining
We present two Bayesian algorithms CD-B and CD-H for discovering unconfounded cause and effect relationships from observational data without assuming causal sufficiency which prec...
Subramani Mani, Constantin F. Aliferis, Alexander ...
HIS
2008
15 years 8 months ago
Multiple Instance Learning with MultiObjective Genetic Programming for Web Mining
This paper introduces a multiobjective grammar based genetic programming algorithm to solve a Web Mining problem from multiple instance perspective. This algorithm, called MOG3P-MI...
Amelia Zafra, Eva Lucrecia Gibaja Galindo, Sebasti...
CIKM
2011
Springer
14 years 6 months ago
MTopS: scalable processing of continuous top-k multi-query workloads
A continuous top-k query retrieves the k most preferred objects in a data stream according to a given preference function. These queries are important for a broad spectrum of appl...
Avani Shastri, Di Yang, Elke A. Rundensteiner, Mat...
KDD
2008
ACM
186views Data Mining» more  KDD 2008»
16 years 6 months ago
Scalable and near real-time burst detection from eCommerce queries
In large scale online systems like Search, eCommerce, or social network applications, user queries represent an important dimension of activities that can be used to study the imp...
Nish Parikh, Neel Sundaresan