Localized Alternative Cluster Ensembles for Collaborative Structuring

  • Michael Wurst
  • Katharina Morik
  • Ingo Mierswa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4212)


Personal media collections are structured in very different ways by different users. Their support by standard clustering algorithms is not sufficient. First, users have their personal preferences which they hardly can express by a formal objective function. Instead, they might want to select among a set of proposed clusterings. Second, users most often do not want hand-made partial structures be overwritten by an automatic clustering. Third, given clusterings of others should not be ignored but used to enhance the own structure. In contrast to other cluster ensemble methods or distributed clustering, a global model (consensus) is not the aim. Hence, we investigate a new learning task, namely learning localized alternative cluster ensembles, where a set of given clusterings is taken into account and a set of proposed clusterings is delivered. This paper proposes an algorithm for solving the new task together with a method for evaluation.


Input Function Cluster Ensemble Query Object Alternative Cluster Music Collection 
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

  • Michael Wurst
    • 1
  • Katharina Morik
    • 1
  • Ingo Mierswa
    • 1
  1. 1.Department of Computer ScienceUniversity of DortmundDortmundGermany

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