Hybrid Intelligent Systems ADAPTIVE GUSTAFSON-KESSEL FUZZY CLUSTERING ALGORITHM BASED ON SELF-LEARNING SPIKING NEURAL NETWORK

The Gustafson-Kessel fuzzy clustering algorithm is capable of detecting hyperellipsoidal clusters of different sizes and orientations by adjusting the covariance matrix of data, thus overcoming the drawbacks of conventional fuzzy c-means algorithm. In this paper, an adaptive version of the Gustafson-Kessel algorithm is proposed. The way to adjust the covariance matrix iteratively is introduced by applying the Sherman-Morrison matrix inversion procedure. The adaptive fuzzy clustering algorithm is implemented on the base of self-learning spiking neural network known as a realistic analog of biological neural systems that can perform fast data processing. Therefore, the proposed fuzzy spiking neural network that belongs to a new type of hybrid intelligent systems makes it possible both to perform fuzzy clustering tasks efficiently and to reduce data processing time considerably.