DB-CSC: A Density-Based Approach for Subspace Clustering in Graphs with Feature Vectors Stephan G¨ unnemann, Brigitte Boden, and Thomas Seidl RWTH Aachen University, Germany - Neighbourhood Statistics - Document - PDFSEARCH.IO

First Page	Meta Content
	DB-CSC: A Density-Based Approach for Subspace Clustering in Graphs with Feature Vectors Stephan G¨ unnemann, Brigitte Boden, and Thomas Seidl RWTH Aachen University, Germany Add to Reading List Document Date: 2015-03-04 00:06:58 Open Document File Size: 1,96 MB Share Result on Facebook Company CSC / / / Facility Thomas Seidl RWTH Aachen University / / IndustryTerm post-processing step / introduced dense subgraph mining techniques / exponential search space containing / dense subgraph mining / social networks / energy consumption / sense in many applications / sensor networks / social network / dense subgraph mining techniques / algorithmic solution / overall processing scheme using diﬀerent pruning techniques / / Organization Thomas Seidl RWTH Aachen University / V (v) / U.S. Securities and Exchange Commission / / Person Thomas Seidl / Brigitte Boden / / Position representative / / Technology clustering algorithm / drug design / / SocialTag Cluster analysis Data mining Geostatistics Neighbourhood Clique Degeneracy Vertex Graph K-means clustering Statistics

DB-CSC: A Density-Based Approach for Subspace Clustering in Graphs with Feature Vectors Stephan G¨ unnemann, Brigitte Boden, and Thomas Seidl RWTH Aachen University, GermanyAdd to Reading List