基于密度敏感距离和模糊划分的改进FCM算法  

FCM Algorithm Based on Density Sensitive Distance and Fuzzy Partition

作　　者：毛森林 夏镇 耿新宇[1] 陈剑辉 蒋宏霞 MAO Sen-lin;XIA Zhen;GENG Xin-yu;CHEN Jian-hui;JIANG Hong-xia(School of Computer Science,Southwest Petroleum University,Chengdu 610500,China)

机构地区：[1]西南石油大学计算机科学学院,成都610500

出　　处：《计算机科学》2022年第S01期285-290,共6页Computer Science

摘　　要：传统的模糊C均值(Fuzzy C-means,FCM)算法对噪声数据敏感,并且在迭代过程中因仅考虑了距离因素,故使用欧氏距离进行距离度量,这会导致只考虑样本点之间的局部一致性特征,而忽略全局一致性特征的问题,为此,提出了一种基于密度敏感距离和模糊划分的改进FCM算法。首先在建立相似度矩阵时使用密度敏感距离替代欧氏距离来进行计算,然后在聚类过程中引入模糊熵作为约束条件,推导出新的聚类中心和具有高斯分布特性的隶属度计算公式。此外,针对传统FCM算法随机选取初始聚类中心可能导致聚类结果不稳定的问题,根据聚类中心点周围样本点比较密集以及聚类中心点之间距离较远两个原则,结合密度敏感距离来选取初始聚类中心点。最后通过实验对比表明,与传统FCM聚类算法及其派生算法相比,改进算法不仅具有更高的聚类性能和抗噪性,且收敛速度也显著提高。The traditional fuzzy C-means(FCM)algorithm is sensitive to noise data and only considers the distance factor in the iterative process.Therefore,the use of Euclidean distance for distance measurement will result in only considering the local consistency feature between sample points,while ignoring the global consistency feature.To solve these problems,an improved FCM algorithm based on density sensitive distance and fuzzy partition is proposed.First,the density sensitive distance is used to replace the Euclidean distance in the calculation of the similarity matrix,and then fuzzy entropy is introduced as a constraint condition in the clustering process to derive the new clustering center and the membership calculation formula with Gaussian distribution characteristics.In addition,in view of the problem that the traditional FCM algorithm randomly selects the initial clustering center may cause the clustering result to be unstable,according to the two principles of denser sample points around the cluster center point and longer distance between the cluster center points,combined with the density sensitive distance to select the initial cluster center point.Finally,the experimental comparison proves that the improved algorithm not only has higher clustering perfor-mance and anti-noise,but also significantly improves the convergence speed compared with the traditional FCM clustering algorithm and its derivative algorithm.

关 键 词：模糊C均值聚类 密度敏感距离 模糊熵 隶属度 初始聚类中心 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]