A novel K-means evolving spiking neural network model for clustering problems

Abstract

In this paper, a novel K-means evolving spiking neural network (KESNN) model for clustering problems has been presented. K-means has been utilised to improve the original ESNN model. This model enhances the flexibility of the ESNN algorithm in producing better solutions to overcoming the disadvantages of K-means. Several standard data sets from UCI machine learning are used for evaluating the performance of this model. It has been found that the K-ESNN provides competitive results in clustering accuracy and speed performance measures compared to the standard K-means. More discussion is provided to prove the effectiveness of the new model in clustering problems.