一种基于量子粒子群算法的模糊c-均值聚类

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．

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

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 algorithm not only has the favorable convergence but also has obviously improved the clustering effect.