Multi-objective K-means evolving spiking neural network model based on differential evolution

In this paper, a multi-objective K-means evolving spiking neural network (MO-KESNN) model based on differential evolution for clustering problems has been presented. K-means has been utilized to improve the ESNN model. This model enhances the flexibility of the ESNN algorithm in producing better solutions which is used to overcome 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 MO-KESNN gives competitive results in clustering accuracy performance and the number of pre-synaptic neurons measure simultaneously compared to the standard K-means. More discussion is provided to prove the effectiveness of the new model in clustering problems.