一种基于量子粒子群算法的模糊c-均值聚类  

A fuzzy c-mean clustering based on quantum-behaved particle swarm optimization

作　　者：王浩[1] 陈蕴[1] 

机构地区：[1]阜阳师范学院计算机与信息学院,安徽阜阳236029

出　　处：《阜阳师范学院学报（自然科学版）》2009年第3期40-43,共4页Journal of Fuyang Normal University(Natural Science)

基　　金：安徽省教育厅自然科学研究项目(2006KJ086B);安徽省计算机实验实训示范中心项目资助

摘　　要：把QPSO算法与模糊c-均值(FCM)算法相结合提出一种混合模糊聚类算法(QPSO-FCM),将FCM算法中基于梯度下降的迭代过程用新算法进行替代,能够在一定程度上克服FCM算法易陷入局部极小的缺陷,降低FCM算法的初值敏感度.通过典型的Wine的数据实验结果证明,改进后的新算法具有良好的收敛性,聚类效果也有一定的改善.The QPSO have the less parameters and higher convergent capability of the global optimizing than Particle Swarm Optimization algorithm （PSO）. A new mixed fuzzy clustering algorithm that Uses Quantum-behaved Particle Swarm Optimization （QPSO） algorithm and combines with Fuzzy C-means （FCM） is proposed in this paper. So the iteration algorithm is replaced by the QPSO based on the gradient descent of FCM, which makes the algorithm have a strong global searching capacity and avoids the local minimum problems of FCM in a way. At the same time, FCM is no longer a large degree dependent on the initialization values. The simulation result proves that compared with FCM the new algo- rithm not only has the favorable convergence but also has obviously improved the clustering effect.

关 键 词：模糊C-均值算法 粒子群算法 量子粒子群算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]