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» Clustering Improves the Exploration of Graph Mining Results
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SDM
2009
SIAM
167views Data Mining» more  SDM 2009»
14 years 4 months ago
Detecting Communities in Social Networks Using Max-Min Modularity.
Many datasets can be described in the form of graphs or networks where nodes in the graph represent entities and edges represent relationships between pairs of entities. A common ...
Jiyang Chen, Osmar R. Zaïane, Randy Goebel
KDD
2001
ACM
181views Data Mining» more  KDD 2001»
14 years 7 months ago
Co-clustering documents and words using bipartite spectral graph partitioning
Both document clustering and word clustering are well studied problems. Most existing algorithms cluster documents and words separately but not simultaneously. In this paper we pr...
Inderjit S. Dhillon
SDM
2009
SIAM
123views Data Mining» more  SDM 2009»
14 years 4 months ago
Randomization Techniques for Graphs.
Mining graph data is an active research area. Several data mining methods and algorithms have been proposed to identify structures from graphs; still, the evaluation of those resu...
Gemma C. Garriga, Kai Puolamäki, Sami Hanhij&...
HICSS
2007
IEEE
134views Biometrics» more  HICSS 2007»
14 years 1 months ago
Exploring the Clinical Notes of Pathology Ordering by Australian General Practitioners: a text mining perspective
A massive rise in the number and expenditure of pathology ordering by general practitioners (GPs) concerns the government and attracts various studies with the aim to understand a...
Zoe Yan Zhuang, Rasika Amarasiri, Leonid Churilov,...
SIGMOD
2010
ACM
196views Database» more  SIGMOD 2010»
13 years 7 months ago
GAIA: graph classification using evolutionary computation
Discriminative subgraphs are widely used to define the feature space for graph classification in large graph databases. Several scalable approaches have been proposed to mine disc...
Ning Jin, Calvin Young, Wei Wang