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Title: Data Clustering and Visualization using Cellular Automata Ants
Authors: Vande Moere, Andrew
Clayden, Justin J.
Issue Date: 2006
Publisher: Springer
Host Document: Australian Joint Conference on Artificial Intelligence pages:826-836
Conference: AI'06 edition:19 location:Hobart, Australia date:4-8 December 2006
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.
ISBN: 978-3-540-49787-5
Publication status: published
KU Leuven publication type: IHb
Appears in Collections:Department of Architecture - miscellaneous

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