Data Clustering and Visualization Using Cellular Automata Ants

  • Andrew Vande Moere
  • Justin J. Clayden
  • Andy Dong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4304)


This paper presents two novel features of an emergent data visualization method coined “cellular ants”: unsupervised data class labeling and shape negotiation. This method merges characteristics of ant-based data clustering and cellular automata to represent complex datasets in meaningful visual clusters. Cellular ants demonstrates how a decentralized multi-agent system can autonomously detect data similarity patterns in multi-dimensional datasets and then determine the according visual cues, such as position, color and shape size, of the visual objects accordingly. Data objects are represented as individual ants placed within a fixed grid, which decide their visual attributes through a continuous iterative process of pair-wise localized negotiations with neighboring ants. The characteristics of this method are demonstrated by evaluating its performance for various benchmarking datasets.


Cellular Automaton Data Item Data Cluster Data Visualization Behavior Rule 
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 2006

Authors and Affiliations

  • Andrew Vande Moere
    • 1
  • Justin J. Clayden
    • 1
  • Andy Dong
    • 1
  1. 1.Key Centre of Design Computing and CognitionThe University of SydneyAustralia

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