Spectral Clustering by Recursive Partitioning

  • Anirban Dasgupta
  • John Hopcroft
  • Ravi Kannan
  • Pradipta Mitra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4168)


In this paper, we analyze the second eigenvector technique of spectral partitioning on the planted partition random graph model, by constructing a recursive algorithm using the second eigenvectors in order to learn the planted partitions. The correctness of our algorithm is not based on the ratio-cut interpretation of the second eigenvector, but exploits instead the stability of the eigenvector subspace. As a result, we get an improved cluster separation bound in terms of dependence on the maximum variance. We also extend our results for a clustering problem in the case of sparse graphs.


Random Graph Separation Condition Singular Vector Spectral Cluster Cluster Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Anirban Dasgupta
    • 1
  • John Hopcroft
    • 2
  • Ravi Kannan
    • 3
  • Pradipta Mitra
    • 3
  1. 1.Yahoo! Research Labs 
  2. 2.Department of Computer ScienceCornell University 
  3. 3.Department of Computer ScienceYale University 

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