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ICDM
2007
IEEE
175views Data Mining» more  ICDM 2007»
14 years 4 months ago
gApprox: Mining Frequent Approximate Patterns from a Massive Network
Recently, there arise a large number of graphs with massive sizes and complex structures in many new applications, such as biological networks, social networks, and the Web, deman...
Chen Chen, Xifeng Yan, Feida Zhu, Jiawei Han
CIKM
2010
Springer
13 years 8 months ago
FacetCube: a framework of incorporating prior knowledge into non-negative tensor factorization
Non-negative tensor factorization (NTF) is a relatively new technique that has been successfully used to extract significant characteristics from polyadic data, such as data in s...
Yun Chi, Shenghuo Zhu
AI
2002
Springer
13 years 9 months ago
Learning Bayesian networks from data: An information-theory based approach
This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop e...
Jie Cheng, Russell Greiner, Jonathan Kelly, David ...
GI
1998
Springer
14 years 2 months ago
Self-Organizing Data Mining
"KnowledgeMiner" was designed to support the knowledge extraction process on a highly automated level. Implemented are 3 different GMDH-type self-organizing modeling algo...
Frank Lemke, Johann-Adolf Müller
IMC
2010
ACM
13 years 7 months ago
Speed testing without speed tests: estimating achievable download speed from passive measurements
How fast is the network? The speed at which real users can download content at different locations and at different times is an important metric for service providers. Knowledge o...
Alexandre Gerber, Jeffrey Pang, Oliver Spatscheck,...