fcm算法实现
A. 模糊c-均值聚类算法的FCM 算法简介
假设样本集合为X={x1 ，x2 ，…，xn }，将其分成c 个模糊组，并求每组的聚类中心cj （ j=1，2，…，C） ，使目标函数达到最小。
B. matlab中的功能函数FCM如何使用
模糊C均值聚类算法，可将输入的数据集data聚为指定的cluster_n类
【函数描述】
语法格式
[center, U, obj_fcn] = FCM(data, cluster_n, options)
用法：
1. [center,U,obj_fcn] = FCM(Data,N_cluster,options);
2. [center,U,obj_fcn] = FCM(Data,N_cluster);
输入变量
data ---- n*m矩阵,表示n个样本,每个样本具有m维特征值
cluster_n ---- 标量,表示聚合中心数目,即类别数
options ---- 4*1列向量，其中
options(1): 隶属度矩阵U的指数，>1(缺省值: 2.0)
options(2): 最大迭代次数(缺省值: 100)
options(3): 隶属度最小变化量,迭代终止条件(缺省值: 1e-5)
options(4): 每次迭代是否输出信息标志(缺省值: 0)
输出变量
center ---- 聚类中心
U ---- 隶属度矩阵
obj_fcn ---- 目标函数值
C. matlab中的fcm算法中的u矩阵怎么初始化
matlab中的fcm算法中的u矩阵怎么初始化
模糊C均值聚类算法，可将输入的数据集data聚为指定的cluster_n类
【函数描述】
语法格式
[center, U, obj_fcn] = FCM(data, cluster_n, options)
用法：
1. [center,U,obj_fcn] = FCM(Data,N_cluster,options);
2. [center,U,obj_fcn] = FCM(Data,N_cluster);
D. 急求FCM算法在C或MATLAB上实现
function [U,V,num_it]=fcm(U0,X)
% MATLAB (Version 4.1) Source Code (Routine fcm was written by Richard J.
% Hathaway on June 21, 1994.) The fuzzification constant
% m = 2, and the stopping criterion for successive partitions is epsilon =??????.
%*******Modified 9/15/04 to have epsilon = 0.00001 and fix univariate bug********
% Purpose:The function fcm attempts to find a useful clustering of the
% objects represented by the object data in X using the initial partition in U0.
%
% Usage: [U,V,num_it]=fcm(U0,X)
%
% where: U0 = on entry, the initial partition matrix of size c x n
% X = on entry, the object data matrix of size s x n
% U = on exit, the final partition matrix of size c x n
% V = on exit, the final prototype matrix of size s x c
% num_it = on exit, the number of iterations done
% Check for legal input values of U0 and X:
%
[c,n]=size(U0);
[s,nn]=size(X);
if min(min(U0)) < 0 | max(max(U0)) > 1 | any(abs(sum(U0) - 1) > .001),
error('U0 is not properly initialized.')
elseif nn ~= n,
error('Dimensions of U0 and X are inconsistent.')
end;
%
% Initialize variables:
%
temp=zeros(c,n); num_it=0; max_it=1000; U=U0; d=zeros(c,n);
epsilon=.00001;min_d=1.0e-100; step_size=epsilon; Vones=zeros(s,n);
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Begin the main loop:
%
while num_it < max_it & step_size >= epsilon,
num_it = num_it + 1;
U0 = U;
%
% Get new V prototypes:
%
temp = U0 .* U0;
work = sum(temp');
V = X*temp';
for i=1:c, V(:,i) = V(:,i) / work(i); end
%
% Get new squared-distance values d:
%
% First, get new initial values for d:
for i=1:c,
for j=1:s,
Vones(j,:)=V(j,i)*ones(1,n);
end
temp = X - Vones;
temp = temp.*temp;
if s > 1,
d(i,:) = sum(temp);
else
d(i,:) = temp;
end
end
% Second, adjust all d values to be at least as big as min_d:
j = find(d < min_d);
d(j) = d(j) - d(j) + min_d;
%
% Get new partition matrix U:
%
U = 1 ./ d;
work = sum(U);
for i=1:c, U(i,:) = U(i,:) ./ work; end
%
% Calculate step_size and return to top of loop:
%
step_size=max(max(abs(U-U0)));
%
% End the main loop:
%
end
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
return
E. 在matlab中做模糊C均值聚类（fcm）算法如何体现初始隶属度
它的程序里面是用rand函数随机初始化了一个矩阵N*c，然后对这个随机矩阵进行归一化，即满足一行（也可能是列记不清楚了），反正是让它满足隶属度的每个样本属于所有类隶属度为1的条件。用这个矩阵进行初始化，计算新的中心 新的隶属度 新的中心。。。。 知道满足阈值。matlab里面自己有函数一招就能找到
F. python 中如何调用FCM算法
以下代码调试通过：
1234567classLuciaClass:#定义类defluciaprint(self,text):#类里面的方法print(' ',text)#方法就是输出textx=LuciaClass()#方法的实例xx.luciaprint('todayisabadday~~~')#实例调用类方法
运行效果：