close all clear all %% test a gray image f_ori=imread('brain.bmp'); fn=imnoise(f_ori,'gaussian',0.03); %% parameters cluster=3; % the number of clustering centers se=3; % the parameter of structuing element used
Firstly, from a global perspective, the typicality values of all the non-winners of samples inside the cluster core are decreased by the suppressed factor while increasing the typicality values of all the winners of samples in the same core. Meanwhile, the typicality values of samples outside ...
[centers,U] = fcm(data) computes cluster centers (centers) and a fuzzy partition matrix (U) for specified data. By default, fcm clusters the data ten times, varying the number of clusters from 2 through 11. [centers,U] = fcm(data,options) specifies clustering options, such as the numbe...
When Nc is auto, the fcm function clusters the data for multiple cluster counts (2 through 11 ) and returns the results for the optimal number of clusters. Nc = ; Specify the exponent the fuzzy partition matrix, which controls the degree of fuzzy overlap between clusters. This value must ...
Fuzzy partitioning is carried out through an iterative optimization of the objective function shown above, with the update of membershipuijand the cluster centerscjby: , This iteration will stop when , where is a termination criterion between 0 and 1, whereaskare the iteration steps. This procedur...
function [center, U, fcn_J]=fcm1(data, cluster_n, options)ifnargin ~= 2 && nargin ~= 3 %判断函数输入变量 error('输入变量的数量不对'); end default_options= [2; 200; 1e-5; 1];%设定默认值%分别是平滑因子,迭代次数,收敛标准,是否显示迭代过程(0为否,1为是)ifnargin == 2 %使用默认...
The method assigns cluster membership of genes initially by random so that the generated clusters can be inconsistent in different runs. On the other hand, the initialization step of our GO Fuzzy c-means algorithm allows the generation of repeatable clustering results and alleviates the need to ...
The original concept is developed to handle data flexibly, that is, a tolerance vector attributes not only to each data but also each cluster. Using the new concept, we can consider the influence of clusters to each data by the tolerance. First, the new concept of tolerance is introduced ...
Dat = xlsread('data.xls'); Feature = zeros(size(Dat)); Feature(:,2:end) = Dat(:,2:end); KCluster = 6; X = Feature(:,2:end); cidx = func_cmeans(X',KCluster); Feature(:,1) = cidx'; %计算五列重的权值最大的两个 W = mean(X,1); [V,I] = sort(W); %选择权值最...
an item can belong to multiple clusters with varying degrees of affinity to the cluster, given by a membership score. Consider thatnitemsxi, wherei= 0, 1,…, (n– 1), in a datasetXare grouped incclusters, given byCjforj= 0, 1,…, (c– 1). Then, each itemxiof the finite setX...