Unsupervised data clustering and image segmentation using natural computing techniques

Natural computing (NC) is a novel approach to solve real life problems inspired in the life itself. A diversity of algorithms had been proposed such as evolutionary techniques, genetic algorithms and particle swarm optimization (PSO). These approaches, together with fuzzy and neural networks, give powerful tools for researchers in a diversity of problems of optimization, classification, data analysis and clustering. This paper presents concepts and experimental results of approaches to data clustering and image segmentation using NC approaches. The main focus are on evolutionary computing, which is based on the concepts of the evolutionary biology and individual-to-population adaptation, and swarm intelligence, which is inspired in the behavior of individuals, together, try to achieve better results for a complex optimization problem. Genetic and PSO based K-means and fuzzy K-means algorithms are described. Results are shown for data clustering using UCI datasets such as Ruspini, Iris and Wine and for image texture and intensity segmentation using images from BrainWeb system.