Data clustering and visualization using cellular automata ants

Authors:
Andrew Vande Moere;Justin J. Clayden;Andy Dong
Affiliations:
Key Centre of Design Computing and Cognition, The University of Sydney, Australia;Key Centre of Design Computing and Cognition, The University of Sydney, Australia;Key Centre of Design Computing and Cognition, The University of Sydney, Australia
Venue:
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Year:
2006

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Abstract

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.