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KDD
2009
ACM
182views Data Mining» more  KDD 2009»
16 years 7 months ago
Scalable graph clustering using stochastic flows: applications to community discovery
Algorithms based on simulating stochastic flows are a simple and natural solution for the problem of clustering graphs, but their widespread use has been hampered by their lack of...
Venu Satuluri, Srinivasan Parthasarathy
KDD
2007
ACM
184views Data Mining» more  KDD 2007»
16 years 7 months ago
GraphScope: parameter-free mining of large time-evolving graphs
How can we find communities in dynamic networks of social interactions, such as who calls whom, who emails whom, or who sells to whom? How can we spot discontinuity timepoints in ...
Jimeng Sun, Christos Faloutsos, Spiros Papadimitri...
KDD
2005
ACM
162views Data Mining» more  KDD 2005»
16 years 7 months ago
Discovering frequent topological structures from graph datasets
The problem of finding frequent patterns from graph-based datasets is an important one that finds applications in drug discovery, protein structure analysis, XML querying, and soc...
Ruoming Jin, Chao Wang, Dmitrii Polshakov, Sriniva...
KDD
2003
ACM
114views Data Mining» more  KDD 2003»
16 years 7 months ago
Information awareness: a prospective technical assessment
Recent proposals to apply data mining systems to problems in law enforcement, national security, and fraud detection have attracted both media attention and technical critiques of...
David Jensen, Matthew J. Rattigan, Hannah Blau
KDD
2002
ACM
157views Data Mining» more  KDD 2002»
16 years 7 months ago
Exploiting unlabeled data in ensemble methods
An adaptive semi-supervised ensemble method, ASSEMBLE, is proposed that constructs classification ensembles based on both labeled and unlabeled data. ASSEMBLE alternates between a...
Kristin P. Bennett, Ayhan Demiriz, Richard Maclin
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