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Unsupervised Non-hierarchical Entropy-based Clustering

  • M. Jardino
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

We present an unsupervised non-hierarchical clustering which realizes a partition of unlabelled objects in K non-overlapping clusters. The interest of this method rests on the convexity of the entropv-based clustering criterion which is demonstrated here. This criterion permits to reach an optimal partition independently of the initial conditions, with a step by step iterative Monte-Carlo process. Several data sets serve to illustrate the main properties of this clustering.

Keywords

Gradient Descent Optimal Partition Vector Element Group Word Speech Technology 
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 2000

Authors and Affiliations

  • M. Jardino
    • 1
  1. 1.Laboratoire d’Informatique pour la Mécanique et les Sciences de l’IngénieurOrsay, CedexFrance

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