基于w-距离均值的模糊聚类算法

针对模糊C-均值(FCM)算法易陷入局部最优值以及对聚类中心和噪声数据敏感问题，提出了一种基于w-距离均值的模糊聚类算法。首先根据数据自身的分布规律，依据样本间距离均值思想确定初始聚类中心，并引入了调衡因子w来调节距离均值阈值；其次为每个样本赋予权值，并利用样本权值修改了聚类中心公式和目标函数公式，提高了算法的抗噪性；最后实验结果验证了所提算法可以有效地解决聚类效果往往受初始聚类中心的影响的问题，避免了局部收敛，增强了抗噪性，准确率和效率较高。

Fuzzy clustering algorithm based on w-mean distance

In this paper,a fuzzy clustering algorithm based on w-mean distance was proposed to solve such defects of Fuzzy C-Means(FCM) algorithm as easily falling into local optimal value and being sensitive to clustering center and noise data.First,initial clustering centers were determined by making use of the idea of the mean distance according to the distribution of data set,and the regulating factor w was introduced to adjust the mean distance.Second,each sample in data set was assigned a weight,and the clustering center formula and target function formula were modified by the weight,so that the anti-noise performance was greatly improved for the algorithm.In the end,the experimental results validate that the proposed algorithm has good effects on selecting initial clustering centers,avoiding local convergence,and having higher performance of anti-noise and effectiveness.