基于改进的模糊C-均值的分级递减聚类算法

提出了基于改进的模糊C-均值的分级递减聚类算法,利用改进的模糊C-均值聚类算法寻找类中心,再自适应确定该类中心的隶属度阈值,将聚类进行分级处理,实现未知类数数据集的聚类。实验结果表明,本算法对未知类数、具有高斯分布的数据集具有聚类效果好、收敛快的特点,且对于类数较多的数据集,本算法也是一种快速聚类算法。

Hierarchical subtractive clustering algorithm based on modified fuzzy C-means

A hierarchical subtractive clustering algorithm based on modified fuzzy C-means is proposed. It finds the cluster centers using modified fuzzy C-means clustering algorithm, gives the membership threshold of the centers adaptively, performs the clustering progressively and then the clustering for the data set with unknown number of clusters is completed. The simulated results show that the proposed algorithm clusters the Gaussian distributed data patterns without a priori information about the number of clusters with good effectiveness and fast clustering convergence.