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Mining Ecological Data with Cellular Automata

Campbell, Alexander B., Pham, Binh L., & Tian, Yu-Chu (2004) Mining Ecological Data with Cellular Automata. In Cellular Automata for Research and Industry 2004, 25-27 October, 2004, University of Amsterdam, Science Park Amsterdam, The Netherlands.

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Abstract

This paper introduces a Cellular Automata (CA) approach to spatiotemporal data mining (STDM). The recently increasing interest in using Genetic Algorithms and other evolutionary techniques to identify CA model parameters has been mainly focused on performing artificial computational tasks such as density classification. This work investigates the potential to extend this research to spatial and spatiotemporal data mining tasks and presents some preliminary experimental results. The purpose is twofold: to motivate and explore an evolutionary CA approach to STDM, and to highlight the suitability of evolutionary CA models to problems that are ostensibly more difficult than, for example, density classification. The problem of predicting wading-bird nest site locations in ecological data is used throughout to illustrate the concepts,and provides the framework for experimental analysis.

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ID Code: 666
Item Type: Conference Paper
Keywords: Cellular Automata, spatiotemporal, data mining, Genetic Algorithms
DOI: 10.1007/b102055
ISBN: 9783540235965
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2004 Springer
Copyright Statement: This is the author-version of the work. Conference proceedings published, by Springer Verlag, will be available via SpringerLink. http://www.springer.de/comp/lncs/ Lecture Notes in Computer Science
Deposited On: 13 Jan 2006
Last Modified: 29 Feb 2012 23:07

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