两阶段模糊C-均值聚类算法

Fuzzy C-Means clustering algorithm with two stages

针对模糊C-均值(Fuzzy C-Means,FCM)算法对初始值敏感、收敛结果易陷入局部极小的问题,本文提出了一种新型的两阶段模糊C-均值聚类算法。算法提出了一种简洁快速的初始聚类中心的选取规则,从而使获得的聚类结果为全局最优。仿真结果证明了该算法的有效性和优越性。

Fuzzy C-Means is one of the algorithms for clustering based on optimizing an objective function, being sensitive to initial conditions, the algorithm usually leads to local minimum results. Aiming at above problem, this paper proposes a new FCM algorithm with two stages. The new algorithm can obtain global optimal solutions through a new simple and efficient select rule of the initial cluster centers. The computer emulate results show the effectivity and superiority of the new algorithm.