SELF-ORGANIZATION IN COMPLEX PATTERN SPACES USING A LOGIC NEURAL-NETWORK

This article investigates the behaviour of a self-organizing logic neu ral network when it is tasked with clustering complex data spaces. The network is based on the discriminator-node structure and is trained u sing an unsupervised-learning adaptation rule. The network performance is evaluated by applying it to clustering tasks involving identifiabl e classes, each of which consists of a large number of distinct subcla sses. The results presented are supported by a statistical analysis, w hich indicates that the system is indeed suited to clustering such com plex data sets.