Parameter-Free Hierarchical Co-clustering by n-Ary Splits

  • Dino Ienco
  • Ruggero G. Pensa
  • Rosa Meo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5781)


Clustering high-dimensional data is challenging. Classic metrics fail in identifying real similarities between objects. Moreover, the huge number of features makes the cluster interpretation hard. To tackle these problems, several co-clustering approaches have been proposed which try to compute a partition of objects and a partition of features simultaneously. Unfortunately, these approaches identify only a predefined number of flat co-clusters. Instead, it is useful if the clusters are arranged in a hierarchical fashion because the hierarchy provides insides on the clusters. In this paper we propose a novel hierarchical co-clustering, which builds two coupled hierarchies, one on the objects and one on features thus providing insights on both them. Our approach does not require a pre-specified number of clusters, and produces compact hierarchies because it makes n −ary splits, where n is automatically determined. We validate our approach on several high-dimensional datasets with state of the art competitors.


Mutual Information Normalize Mutual Information Rand Index Adjust Rand Index Cluster Hierarchy 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Dino Ienco
    • 1
  • Ruggero G. Pensa
    • 1
  • Rosa Meo
    • 1
  1. 1.Department of Computer ScienceUniversity of TorinoTurinItaly

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