A novel linear cellular automata-based data clustering algorithm

  • Authors:

  • Javier de Lope;Darío Maravall

  • Affiliations:

  • Cognitive Robotics Group, Dept. of Artificial Intelligence, Universidad Politécnica de Madrid and Dept. Applied Intelligent Systemsm, Universidad Politécnica de Madrid;Cognitive Robotics Group, Dept. of Artificial Intelligence, Universidad Politécnica de Madrid

  • Venue:
  • IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
  • Year:
  • 2011

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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.