基于半监督的模糊C-均值聚类算法

Fuzzy C-Means Clustering Algorithm Based on Semi-supervised Learning

通过将半监督学习的思想引入到模糊C-均值聚类方法中,提出一种基于半监督的模糊C-均值聚类算法,有效解决了模糊C-均值聚类算法随机选取初始聚类中心导致聚类结果局部收敛的问题,能客观获取最佳聚类数目和初始聚类中心.实验结果表明,与传统模糊C-均值聚类算法相比,基于半监督的模糊C-均值算法在一定程度上减少了迭代次数,降低了对初始聚类中心的依赖性.

A fuzzy C-means clustering algorithm based on semi-supervised learning was proposed by introducing semi-supervised learning into fuzzy C-means clustering algorithm.It has effectively solved the problem that the initial clustering centers random selection of fuzzy C-means algorithm can easily cause the local convergence and affects the clustering.The proposed algorithm can objectively obtain the optimal number of clusters and the initial cluster centers.Compared with the traditional FCM,our method can reduce the number of iterations and the dependence on initial cluster centers.