Self-Organizing Data Clustering: A Novel Stochastic Generalized Cellular Automata

A random generalized cellular automata (GCA) for self-organizing data clustering is proposed. Using GCA, we transform the data clustering into a stochastic process over the configuration space of cellular states on a GCA array. The proposed approach has many advantages in terms of the self-organizing clustering, insensitivity to noise, quality robustness to clustered data, suitability for high-dimensional and massive data sets, learning ability, and the easier hardware implementation with the VLSI systolic technology. The simulations and comparisons have shown the effectiveness and good performance of the proposed GCA approach to data clustering