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» A Fully Distributed Framework for Cost-Sensitive Data Mining
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ICDCS
2002
IEEE
12 years 2 months ago
A Fully Distributed Framework for Cost-Sensitive Data Mining
Data mining systems aim to discover patterns and extract useful information from facts recorded in databases. A widely adopted approach is to apply machine learning algorithms to ...
Wei Fan, Haixun Wang, Philip S. Yu, Salvatore J. S...
SDM
2009
SIAM
117views Data Mining» more  SDM 2009»
12 years 7 months ago
Spatially Cost-Sensitive Active Learning.
In active learning, one attempts to maximize classi´Čüer performance for a given number of labeled training points by allowing the active learning algorithm to choose which points...
Alexander Liu, Goo Jun, Joydeep Ghosh
SIGSOFT
2008
ACM
12 years 10 months ago
Javert: fully automatic mining of general temporal properties from dynamic traces
Program specifications are important for many tasks during software design, development, and maintenance. Among these, temporal specifications are particularly useful. They expres...
Mark Gabel, Zhendong Su
ICML
2008
IEEE
12 years 10 months ago
Fully distributed EM for very large datasets
In EM and related algorithms, E-step computations distribute easily, because data items are independent given parameters. For very large data sets, however, even storing all of th...
Jason Wolfe, Aria Haghighi, Dan Klein
KDD
2009
ACM
198views Data Mining» more  KDD 2009»
12 years 10 months ago
Pervasive parallelism in data mining: dataflow solution to co-clustering large and sparse Netflix data
All Netflix Prize algorithms proposed so far are prohibitively costly for large-scale production systems. In this paper, we describe an efficient dataflow implementation of a coll...
Srivatsava Daruru, Nena M. Marin, Matt Walker, Joy...
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