Data clustering using a linear cellular automata-based algorithm

In this paper we propose a novel data clustering algorithm based on the idea of considering the individual data items as cells belonging to a uni-dimensional cellular automaton. Our proposed algorithm combines insights into 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 also consider an automatic method for determining the number of clusters in the dataset by analyzing the intra-cluster variances. A series of experiments with both synthetic and real datasets are presented in order to study empirically the convergence and performance results. These experimental results are compared to the obtained by conventional clustering algorithms.