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)


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.


Topographic Mapping Input Sample Neighbourhood Function Output Density Minimal Path Length 
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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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