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The fuzzy clustering algorithm has been widely used in the research area and production and life. However, the conventional fuzzy algorithms have a disadvantage of high computational complexity. This article proposes an improved fuzzy C-means (FCM) algorithm based on K-means and principle of granularity. This algorithm is aiming at solving the problems of optimal number of clusters and sensitivity to the data initialization in the conventional FCM methods. The initialization stage of the K-medoid cluster, which is different from others, has a strong representation and is capable of detecting data with different sizes. Meanwhile, through the combination of the granular computing and FCM, the optimal number of clusters is obtained by choosing accurate validity functions. Finally, the detailed clustering process of the proposed algorithm is presented, and its performance is validated by simulation tests. The test results show that the proposed improved FCM algorithm has enhanced clustering performance in the computational complexity, running time, cluster effectiveness compared with the existing FCM algorithms.

In order to improve the performance of network traffic prediction model, a novel network traffic prediction model is proposed in this paper which embedding dimension and time delay of network traffic time series are jointly optimized by genetic algorithm. The optimail embedding dimension and time delay are used to establish the one-step and multi-step based on RBF neural network, finally, the simulation experiments are carried out to test the performance of the proposed model. The results show that the proposed model can select the optimal embedding dimension, delay time, and can significantly improve the prediction accuracy of network traffic, and the prediction resresults is better than reference models.

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