[C,U,objFun,info] = fcm(dataset,options); C contains the computed centers for each cluster. U contains the computed fuzzy partition matrix, which indicates the degree of membership of each data point within each cluster. If Nc is auto, then C and U contain the clustering results for the...
load fcmdata.dat Specify clustering options using an fcmOptions object. For this example, set the number of clusters to 2 and use default values for the other options. Get options = fcmOptions(NumClusters=2); Find the cluster centers using fuzzy c-means clustering. Get [centers,U] = ...
Fuzzy C-means (FCM) clustering algorithm is an important and popular clustering algorithm which is utilized in various application domains such as pattern recognition, machine learning, and data mining. Although this algorithm has shown acceptable performance in diverse problems, the current literature ...
四、参考文献[1] Bezdek J C. A convergence theorem for the fuzzy ISODATA clustering algorithms, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1980, 2: 1-8. [2] Bezdek J C, Hathaway R J, Sabin M J, et al. Convergence theory for fuzzy c-means: counter-examples and repa...
浅谈模糊C均值聚类(Fuzzy C-means Clustering) 定义:模糊c-均值聚类算法 fuzzy c-means algorithm (FCMA)或称( FCM)。在众多模糊聚类算法中,模糊C-均值( FCM) 算法应用最广泛且较成功,它通过优化目标函数得到每个样本点对所有类中心的隶属度,从而决定样本点的类属以达到自动对样本数据进行分类的目的。 假设样本集...
Fuzzy c-means (FCM) is a method of clustering which allows one piece of data to belong to two or more clusters. This method (developed byDunn in 1973and improved byBezdek in 1981) is frequently used in pattern recognition. It is based on minimization of the following objective function: ...
FCM 是一种有目标的模糊聚类算法,其目标函数为: Jm(U,v)=∑i=1c∑k=1nxikm‖xk−vi‖2 式中,v = ( v1,v2,…,vc) ,m > 1 为模糊参数,该参数决 定了聚类的模糊度,也就是数据点可以成为多个类的程度,大多数情况下m = 2。当式( 2) 取到最小值时结果最优。对于聚类中心,其取值为 ...
Fuzzy C-means clustering algorithm combined with markov random field for brain MR image segmentation [ J ]. Journal of Image and Graphics, 2012, 17(12) : 1554-1560.佘黎煌,钟华,张石.结合马尔可夫随机场与模糊C-均值聚类的脑MRI图像分割[J]. 中国图象图形学报.2012(12)...
模糊C均值聚类(Fuzzy C-means)算法简称FCM算法,是软聚类方法的一种。FCM算法最早由Dunn在1974年提出然后经 Bezdek推广。 硬聚类算法在分类时有一个硬性标准,根据该标准进行划分,分类结果非此即彼。 软聚类算法更看重隶属度,隶属度在[0,1]之间,每个对象都有属于每个类的隶属度,并且所有隶属度之和为 1,即更接近...
三FCM的Matlab实现 function [U,P,Dist,Cluster_Res,Obj_Fcn,iter]=fuzzycm(Data,C,plotflag,M,epsm) % 模糊 C 均值聚类 FCM: 从随机初始化划分矩阵开始迭代 % [U,P,Dist,Cluster_Res,Obj_Fcn,iter] = fuzzycm(Data,C,plotflag,M,epsm)