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ITCH: Information-Theoretic Cluster Hierarchies

  • Christian Böhm
  • Frank Fiedler
  • Annahita Oswald
  • Claudia Plant
  • Bianca Wackersreuther
  • Peter Wackersreuther
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6321)

Abstract

Hierarchical clustering methods are widely used in various scientific domains such as molecular biology, medicine, economy, etc. Despite the maturity of the research field of hierarchical clustering, we have identified the following four goals which are not yet fully satisfied by previous methods: First, to guide the hierarchical clustering algorithm to identify only meaningful and valid clusters. Second, to represent each cluster in the hierarchy by an intuitive description with e.g. a probability density function. Third, to consistently handle outliers. And finally, to avoid difficult parameter settings.With ITCH, we propose a novel clustering method that is built on a hierarchical variant of the information-theoretic principle of Minimum Description Length (MDL), referred to as hMDL. Interpreting the hierarchical cluster structure as a statistical model of the data set, it can be used for effective data compression by Huffman coding. Thus, the achievable compression rate induces a natural objective function for clustering, which automatically satisfies all four above mentioned goals.

Keywords

Probability Density Function Hierarchical Cluster Gaussian Mixture Model Single Link Minimum Description Length 
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

  • Christian Böhm
    • 1
  • Frank Fiedler
    • 1
  • Annahita Oswald
    • 1
  • Claudia Plant
    • 2
  • Bianca Wackersreuther
    • 1
  • Peter Wackersreuther
    • 1
  1. 1.University of MunichMunichGermany
  2. 2.Florida State UniversityTallahasseeUSA

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