c-Means Clustering on the Multinomial Manifold

  • Ryo Inokuchi
  • Sadaaki Miyamoto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4617)

Abstract

In this paper, we discuss c-means clustering algorithms on the multinomial manifold. Data forms a Riemannian manifold with the Fisher information metric via the probabilistic mapping from datum to a probability distribution. For discrete data, the statistical manifold of the multinomial distribution is appropriate. In general, The euclidean distance is not appropriate on the manifold because the parameter space of the distribution is not flat. We apply the Kullback-Leibler (KL) divergence or the Hellinger distance as approximations of the geodesic distance to hard c-means and fuzzy c-means.

Keywords

Cluster Center Fisher Information Geodesic Distance Multinomial Distribution Lagrange Multiplier 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 2007

Authors and Affiliations

  • Ryo Inokuchi
    • 1
  • Sadaaki Miyamoto
    • 2
  1. 1.Doctoral Program in Risk Engineering, University of Tsukuba, Ibaraki 305-8573Japan
  2. 2.Department of Risk Engineering, University of Tsukuba, Ibaraki 305-8573Japan

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