A neuro-fuzzy Kohonen network for data stream possibilistic clustering and its online self-learning procedure

Abstract

A task of Data Stream Fuzzy Clustering is considered when data is processed sequentially under a priori uncertainty conditions about both a number of clusters and a degree of clusters’ overlapping. A modified two-layer neuro-fuzzy Kohonen network is used for solving the possibilistic fuzzy clustering tasks. This system tunes centers’ coordinates and membership levels of every pattern to clusters during the self-learning procedure and automatically increases a number of neurons during data processing. A distinguishing feature of the proposed approach is its computational simplicity due to the fact that a recurrent modification of the possibilistic fuzzy clustering procedure is used for tuning the network’s parameters. The proposed neuro-fuzzy system is based on the concepts of evolving systems of Computational Intelligence, the recurrent optimization, the possibilistic fuzzy clustering, and Data Stream Mining.