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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&...
SDM
2009
SIAM
114views Data Mining» more  SDM 2009»
14 years 4 months ago
Top-k Correlative Graph Mining.
Correlation mining has been widely studied due to its ability for discovering the underlying occurrence dependency between objects. However, correlation mining in graph databases ...
Yiping Ke, James Cheng, Jeffrey Xu Yu
ICDE
2009
IEEE
290views Database» more  ICDE 2009»
14 years 9 months ago
GraphSig: A Scalable Approach to Mining Significant Subgraphs in Large Graph Databases
Graphs are being increasingly used to model a wide range of scientific data. Such widespread usage of graphs has generated considerable interest in mining patterns from graph datab...
Sayan Ranu, Ambuj K. Singh
PKDD
2010
Springer
127views Data Mining» more  PKDD 2010»
13 years 5 months ago
Software-Defect Localisation by Mining Dataflow-Enabled Call Graphs
Defect localisation is essential in software engineering and is an important task in domain-specific data mining. Existing techniques building on call-graph mining can localise dif...
Frank Eichinger, Klaus Krogmann, Roland Klug, Klem...