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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Moere, A. V., Clayden, J. J., & Dong, A. (2006). Data clustering and visualization using cellular automata ants. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4304 LNAI, pp. 826–836). Springer Verlag. https://doi.org/10.1007/11941439_87
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