基于粒子群优化算法的模糊C-均值聚类

利用粒子群优化（PSO）算法全局寻优、 快速收敛的特点, 结合模糊C 均值（FCM）算法提出一种新的模糊聚类算法. 新算法用PSO算法代替了FCM算法的基于梯度下降的迭代过程, 使算法具有很强的全局搜索能力, 很大程度上避免了FCM算法易陷入局部极小的缺陷； 同时也降低了FCM算法对初始值的敏感度. 实验结果表明, 与FCM相比本文算法聚类更为准确, 效率更高.

Fuzzy C-Mean Clustering Based on Particle Swarm Optimization

A novel fuzzy clustering algorithm which uses the merits of the global optimizing and higher convergent speed of Particle Swarm Optimization(PSO) algorithm and combines with Fuzzy C-means(FCM) is(proposed). The iteration process is replaced by the PSO 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.At the same time,FCM is no longer a large degree dependent on the initialization values.Numerical experiments show that the proposed algorithm is more accurate and efficient than FCM.