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The Architecture of Ant-Based Clustering to Improve Topographic Mapping

  • Lutz Herrmann
  • Alfred Ultsch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5217)

Abstract

This paper analyzes the popular ant-based clustering approach of Lumer/Faieta. Analysis of formulae unveils that ant-based clustering is strongly related to Kohonen’s Self-Organizing Batch Map. Known phenomena, e.g. formation of too many and too small clusters, can be explained due to that. Furthermore it is shown how topographic mapping of ant-based methods is substantially improved by means of a modified error function. This is demonstrated on few selected fundamental clustering problems.

Keywords

Topographic Mapping Input Sample Neighbourhood Function Output Density Minimal Path 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 2008

Authors and Affiliations

  • Lutz Herrmann
    • 1
  • Alfred Ultsch
    • 1
  1. 1.Databionics Research Group, Dept. of Mathematics and Computer ScienceUniversity of Marburg 

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