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ICPR
2010
IEEE
14 years 9 months ago
Semi-supervised Graph Learning: Near Strangers or Distant Relatives
In this paper, an easily implemented semi-supervised graph learning method is presented for dimensionality reduction and clustering, using the most of prior knowledge from limited...
Weifu Chen, Guocan Feng
ICDM
2010
IEEE
230views Data Mining» more  ICDM 2010»
14 years 9 months ago
Clustering Large Attributed Graphs: An Efficient Incremental Approach
In recent years, many networks have become available for analysis, including social networks, sensor networks, biological networks, etc. Graph clustering has shown its effectivenes...
Yang Zhou, Hong Cheng, Jeffrey Xu Yu
SDM
2010
SIAM
213views Data Mining» more  SDM 2010»
15 years 1 months ago
Spectral Analysis of Signed Graphs for Clustering, Prediction and Visualization
We study the application of spectral clustering, prediction and visualization methods to graphs with negatively weighted edges. We show that several characteristic matrices of gra...
Jérôme Kunegis, Stephan Schmidt, Andr...
71
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AUSDM
2007
Springer
107views Data Mining» more  AUSDM 2007»
15 years 5 months ago
A Discriminant Analysis for Undersampled Data
One of the inherent problems in pattern recognition is the undersampled data problem, also known as the curse of dimensionality reduction. In this paper a new algorithm called pai...
Matthew Robards, Junbin Gao, Philip Charlton
BMCBI
2007
265views more  BMCBI 2007»
14 years 11 months ago
Large scale clustering of protein sequences with FORCE -A layout based heuristic for weighted cluster editing
Background: Detecting groups of functionally related proteins from their amino acid sequence alone has been a long-standing challenge in computational genome research. Several clu...
Tobias Wittkop, Jan Baumbach, Francisco P. Lobo, S...