Density-Based Subspace Clustering in Heterogeneous Networks

  • Brigitte Boden
  • Martin Ester
  • Thomas Seidl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8724)


Many real-world data sets, like data from social media or bibliographic data, can be represented as heterogeneous networks with several vertex types. Often additional attributes are available for the vertices, such as keywords for a paper. Clustering vertices in such networks, and analyzing the complex interactions between clusters of different types, can provide useful insights into the structure of the data. To exploit the full information content of the data, clustering approaches should consider the connections in the network as well as the vertex attributes. We propose the density-based clustering model TCSC for the detection of clusters in heterogeneous networks that are densely connected in the network as well as in the attribute space. Unlike previous approaches for clustering heterogeneous networks, TCSC enables the detection of clusters that show similarity only in a subset of the attributes, which is more effective in the presence of a large number of attributes.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Brigitte Boden
    • 1
  • Martin Ester
    • 2
  • Thomas Seidl
    • 1
  1. 1.RWTH Aachen UniversityAachenGermany
  2. 2.Simon Fraser UniversityBurnabyCanada

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