Cluster ensemble techniques are a means for boosting the clustering performance. However, many cluster ensemble methods are faced with high computational complexity. Indeed, the median partition methods are \(\mathcal{NP}\)-complete. While a variety of approximative approaches for suboptimal solutions have been proposed in the literature, the performance evaluation is typically done by means of ground truth. In contrast this work explores the question how well the cluster ensemble methods perform in an absolute sense without ground truth, i.e. how they compare to the (unknown) optimal solution. We present a study of applying and extending a lower bound as an attempt to answer the question. In particular, we demonstrate the tightness of the lower bound, which indicates that there exists no more room for further improvement (for the particular data set at hand). The lower bound can thus be considered as a means of exploring the performance limit of cluster ensemble techniques.


Distance Function Ground Truth Cluster Ensemble Consensus Cluster Cluster Ensemble Method 
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 2010

Authors and Affiliations

  • Xiaoyi Jiang
    • 1
  • Daniel Abdala
    • 1
  1. 1.Department of Mathematics and Computer ScienceUniversity of MünsterMünsterGermany

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