Spatiotemporal Data Mining with Cellular Automata

  • Karl Fu
  • Yang Cai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3991)


In this paper, we describe a cellular automata model for predicting biological spatiotemporal dynamics in an imagery data flow. The Bayesian probability-based algorithm is used to estimate the algal formation in a two-dimensional space. The dynamics of the cellular artificial life is described with diffusion, transport, collision and deformation. We tested the model with the historical data, including parameters, such as time, position and temperature.


Cellular Automaton Cellular Automaton Artificial Life Cellular Automaton Model Rigid Object 
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

  • Karl Fu
    • 1
  • Yang Cai
    • 1
  1. 1.Visual Intelligence Studios, Cylab, CIC-2218Carnegie Mellon UniversityPittsburghUSA

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