The Architecture of Ant-Based Clustering to Improve Topographic Mapping

Authors:
Lutz Herrmann;Alfred Ultsch
Affiliations:
Databionics Research Group, Dept. of Mathematics and Computer Science, University of Marburg,;Databionics Research Group, Dept. of Mathematics and Computer Science, University of Marburg,
Venue:
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Year:
2008

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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.