一种结合自适应近邻与密度峰值的加权模糊聚类算法  

Weighted Fuzzy Clustering Algorithm Combining Adaptive Nearest Neighbors and Density Peaks

作　　者：张春昊 解滨[1,2,3] 张喜梅[1] 徐童童 ZHANG Chun-hao;XIE Bin;ZHANG Xi-mei;XU Tong-tong(College of Computer and Cyber Security,Hebei Normal University,Shijiazhuang 050024,China;Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics&Data Security,Hebei Normal University,Shijiazhuang 050024,China;Hebei Provincial Key Laboratory of Network&Information Security,Hebei Normal University,Shijiazhuang 050024,China)

机构地区：[1]河北师范大学计算机与网络空间安全学院,石家庄050024 [2]河北师范大学供应链大数据分析与数据安全河北省工程研究中心,石家庄050024 [3]河北师范大学河北省网络与信息安全重点实验室,石家庄050024

出　　处：《小型微型计算机系统》2023年第9期1974-1982,共9页Journal of Chinese Computer Systems

基　　金：国家自然科学基金项目(62076088)资助;河北师范大学技术创新基金项目(L2020K09)资助.

摘　　要：传统的模糊C均值(fuzzy c-means,FCM)算法的聚类结果容易受到随机选取初始聚类中心的影响,且在聚类过程中忽视了样本的不同特征和样本本身的重要程度对聚类结果产生的影响.针对这一系列问题,提出了一种结合自适应近邻与密度峰值的基于信息熵加权的模糊聚类算法(ANNDP-WFCM).首先,结合自适应近邻的密度峰值算法(ANNDP)实现初始聚类中心的自动搜索,针对不同规模、不同结构的数据集可以自适应的找到每个样本的近邻集合,根据近邻信息定义样本的局部密度,搜索和发现数据集中的密度峰值点作为初始聚类中心.然后通过信息熵赋权区分不同特征在聚类过程中的重要程度,同时利用样本之间距离的倒数对样本本身进行加权,重新定义目标函数中的模糊聚类中心.最后针对目标函数,利用拉格朗日乘子法交替寻优,对最终的隶属度矩阵去模糊化得到聚类结果.通过不同公共数据集的对比实验,验证了ANNDP-WFCM算法具有较少的迭代次数和较高的聚类准确性.The clustering results of traditional fuzzy c-means(FCM)algorithm are easily affected by the random selection of initial clustering centers,and the influence of different features of samples and the importance of samples on the clustering results are ignored in the clustering process.Aiming at this series of problems,a fuzzy clustering algorithm based on information entropy weighting(ANNDP-WFCM)combined with adaptive nearest neighbors and density peaks is proposed.Firstly,the automatic search of the initial clustering centers is realized by combining the adaptive nearest neighbors density peaks algorithm(ANNDP).The nearest neighbors of each sample can be adaptively found for data sets with different scales and structures.The local density of the sample is defined according to the nearest neighbors information,and the density peak points in the data set are searched and found as the initial clustering centers.Then,the importance of different features in the clustering process is distinguished by information entropy weighting.At the same time,the reciprocal of distance between samples is used to weight the sample itself,and the fuzzy clustering centers in the objective function are redefined.Finally,for the objective function,the Lagrange multiplier method is used to alternately optimize the final membership matrix to get the clustering results.Through comparative experiments on different public datasets,it is verified that the ANNDP-WFCM algorithm has fewer iterations and higher clustering accuracy.

关 键 词：FCM 密度峰值 自适应 信息熵 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]