Stochastic Competitive Learning Applied to Handwritten Digit and Letter Clustering

Competitive learning is an important mechanism for data clustering and pattern recognition. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large scale networks. In this model, several particles walk in the network and compete with each other to occupy as many nodes as possible, while attempting to reject intruder particles. As a result, each particle will dominate a cluster of the network. Moreover, we propose an efficient method for determining the right number of clusters by using the information generated by the competition process itself, avoiding the calculation of an external evaluating index. In this work, we apply the model to handwritten data clustering. Computer simulations reveal that the proposed technique obtains satisfactory cluster detection accuracy.