Unsupervised clustering using self-optimizing neural networks

Abstract

Self-optimizing neural networks (SONNs) are very effective in solving different classification tasks. They have been successfully used to many different problems. The classical SONN adaptation process has been defined as supervised. This paper introduces a new very interesting SONN feature - the unsupervised clustering ability. The unsupervised SONNs (US-SONNs) are able to find out most differentiating features for some training data and recursively divide them into subgroups. US-SONNs can also characterize the importance of features differentiating these groups. The division of the data is recursively performed till the data in subgroups differ imperceptibly. The SONN clustering proceeds very fast in comparison to other unsupervised clustering methods.