基于PSO的模糊C均值聚类算法

Pso-based Fuzzy C-Mean Clustering Algorithm

在分析模糊C均值聚类算法存在不足的基础上,提出了一种新的聚类算法:基于粒子群的模糊C均值聚类算法.该算法利用粒子群强大的全局寻优能力,不仅克服了传统的模糊C均值聚类算法对初始值敏感、噪声数据敏感、易陷入局部最优的问题,而且有较快的收敛速度.试验证明,这种算法是一种很有潜力的模糊聚类算法.

After analyzing the disadvantages of the fuzzy C-mean clustering algorithm, this paper proposes a novel Fuzzy C-Mean Clustering based on Particle Swarm Optimization algorithm. This algorithm not only avoids the local optima and is robust to initialization, but also increases the convergence speed and has global searching cap ability. The experimental results show that the algorithm has much potential.