A Novel Linear Cellular Automata-Based Data Clustering Algorithm

  • Javier de Lope
  • Darío Maravall
Conference paper

DOI: 10.1007/978-3-642-21344-1_8

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6686)
Cite this paper as:
de Lope J., Maravall D. (2011) A Novel Linear Cellular Automata-Based Data Clustering Algorithm. In: Ferrández J.M., Álvarez Sánchez J.R., de la Paz F., Toledo F.J. (eds) Foundations on Natural and Artificial Computation. IWINAC 2011. Lecture Notes in Computer Science, vol 6686. Springer, Berlin, Heidelberg

Abstract

In this paper we propose a novel data clustering algorithm based on the idea of considering the individual data items as cells belonging to an uni-dimensional cellular automaton. Our proposed algorithm combines insights from both social segregation models based on Cellular Automata Theory, where the data items themselves are able to move autonomously in lattices, and also from Ants Clustering algorithms, particularly in the idea of distributing at random the data items to be clustered in lattices. We present a series of experiments with both synthetic and real datasets in order to study empirically the convergence and performance results. These experimental results are compared to the obtained by conventional clustering algorithms.

Keywords

Cellular Automata Machine Learning Pattern Recognition Data Mining Data Clustering Social Segregation Models Ants Clustering 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Javier de Lope
    • 1
    • 2
  • Darío Maravall
    • 1
  1. 1.Cognitive Robotics Group, Dept. of Artificial IntelligenceUniversidad Politécnica de MadridSpain
  2. 2.Dept. Applied Intelligent SystemsUniversidad Politécnica de MadridSpain

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