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IJMMS
2007
166views more  IJMMS 2007»
13 years 9 months ago
Visualization of large networks with min-cut plots, A-plots and R-MAT
What does a ‘normal’ computer (or social) network look like? How can we spot ‘abnormal’ sub-networks in the Internet, or web graph? The answer to such questions is vital f...
Deepayan Chakrabarti, Christos Faloutsos, Yiping Z...
SDM
2007
SIAM
126views Data Mining» more  SDM 2007»
13 years 11 months ago
Nonlinear Dimensionality Reduction using Approximate Nearest Neighbors
Nonlinear dimensionality reduction methods often rely on the nearest-neighbors graph to extract low-dimensional embeddings that reliably capture the underlying structure of high-d...
Erion Plaku, Lydia E. Kavraki
ICML
2007
IEEE
14 years 10 months ago
Gradient boosting for kernelized output spaces
A general framework is proposed for gradient boosting in supervised learning problems where the loss function is defined using a kernel over the output space. It extends boosting ...
Florence d'Alché-Buc, Louis Wehenkel, Pierr...
ICDM
2008
IEEE
120views Data Mining» more  ICDM 2008»
14 years 4 months ago
Anti-monotonic Overlap-Graph Support Measures
In graph mining, a frequency measure is anti-monotonic if the frequency of a pattern never exceeds the frequency of a subpattern. The efficiency and correctness of most graph pat...
Toon Calders, Jan Ramon, Dries Van Dyck
ICPR
2010
IEEE
14 years 4 months ago
A Graph Matching Algorithm using Data-Driven Markov Chain Monte Carlo Sampling
We propose a novel stochastic graph matching algorithm based on data-driven Markov Chain Monte Carlo (DDMCMC) sampling technique. The algorithm explores the solution space efficien...
Jungmin Lee, Minsu Cho, Kyoung Mu Lee