关键词: 网络虚拟环境, 用户访问模式聚类, 多目标粒子群优化, 模糊C均值

Abstract:

In order to efficiently implement  personalized information services in network virtual environments, a new clustering algorithm for user access patterns was proposed, which was  the MPF, i.e. the fuzzy C-means (FCM) clustering algorithm based on multi-objects particle swarm optimization (MOPSO). The MPF could combine the respective advantages of PSO and FCM. Through the global spatial search of PSO, it could avoid that  FCM was susceptible to initial value, noisy data and easily falling into the local optimum. In order to improve the clustering effect,  a particle fitness function was designed based on dualobjectives (intra-class distance and inter-class distance) in PSO. Finally, the standard data set and simulation data set were applied to verify the effectiveness of this MPF. Experimental results showed that this algorithm had  good performance in clustering precision.

Key words: network virtual environments, user access patterns, multi-objects particle swarm optimization, fuzzy C-means