中文摘要：

针对模糊C-均值（FCM）算法对初始聚类中心和噪声数据敏感的缺陷,提出一种基于大密度区域的模糊聚类算法.该算法首先利用大密度区域以及样本的密度值变化方法,选取初始聚类中心以及候选初始聚类中心,并依据初始聚类中心与候选初始聚类中心的距离,确定初始聚类中心点,从而有效的克服了随机给定初始聚类中心容易使算法收敛到局部极小的缺陷;其次,分别利用密度函数为样本加权和引用改进的隶属度函数进行优化,有效地提高了模糊聚类的抗噪性;最后实验验证了算法在初始聚类中心的确定,聚类效果和抗噪性方面具有良好的效果.

英文摘要：

For the defects of fuzzy c-means（FCM） algorithm which are random of initial clustering center and noise data sensibility,a fuzzy clustering algorithm is presented by using large density region in the paper.Firstly,the algorithm selects initial clustering centers and the candidate initial clustering centers by making use of the large density region and change of samples′ density values,then the initial clustering centers based on the distance of initial clustering centers and the candidate initial clustering centers are determined,so that it effectively overcome the defect that given randomly initial clustering center make FCM algorithms converging to local minimum easily.Secondly,the algorithm uses density function as samples′ weights and optimizes its membership function,so that the algorithm′s ability of anti-noise is improved.In the end,the experimental results validate that the algorithm has good effect in selecting initial clustering center,clustering effect and ability of anti-noise.