Data clustering with stochastic cellular automata

Data clustering is a well studied problem, where the aim is to partition a group of data instances into a number of clusters. Various methods have been proposed for the problem. K-means and its variants are the most well known examples. A common characteristic shared by the clustering algorithms is that they are all based on distance calculations between data points, or between data points and centroids. Hence, the efficiency of the proposed methods decline when big data is clustered. Clustering algorithms based on cellular automata have also been proposed in the literature. However, these methods are based on distance calculations, too. In this study, a new approach is proposed for the clustering problem. The method is based on the formation of clusters in a cellular automata by the interaction of neighborhood cells. The data points are mapped to fixed cellular automata cells, and the clusters are formed in a parallel fashion. The initial clusters formed spread in the cellular automata by uniting neighborhood cells in the same cluster. The rules utilized to compose clusters in the automata are inspired by the heat transfer process in nature. No distance calculation is used during the procedure. Therefore, it is possible to cluster huge datasets within a reasonable amount of time with the method proposed.