Self-organization in complex pattern spaces using a logic neural network

This article investigates the behaviour of a self-organizing logic neural network when it is tasked with clustering complex data spaces. The network is based on the discriminator-node structure and is trained using an unsupervised-learning adaptation rule. The network performance is evaluated by applying it to clustering tasks involving identifiable classes, each of which consists of a large number of distinct subclasses. The results presented are supported by a statistical analysis, which indicates that the system is indeed suited to clustering such complex data sets.