dfknc.m

其中有五个程序

                             %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
                             %          程序运行过程中调用:d函数,这个函数计算在本聚类模式下的两个模糊数的距离           
                             %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

function DFKNC(c,mm,x,e,a,b)%其中，e表示预设的阈值；c类，mm表示模糊关系数，
                             %x为待聚类的模糊数据矩阵（依次为中心，左区间长和右区间长），每一行表示一个模糊数;a,b分别充当lambda和rho的角色
n=size(x);
n=n(1);  %求出x的模糊数的个数n
                             %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
                             %       判断类别数是不是大于数据个数      %
                             %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if c>n
    fprintf('ERROR! The number of clusters is greater than the number of the data, namely %d>%d. Please revise your cluster number!\n',c,n)
else
    
    
                             %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
                             % 以下为定义变量部分：                                                                     
                             %    第一部分声明初始关系矩阵空间                                                         
                             %    第二部分声明输入数据的中心，左区间长，右区间长三个空间                                                                  
                             %    第三部分声明初始类中心的中心，左区间长，右区间长 三个空间                                
                             %    第四部分对以上的两个中心，左区间长，右区间长的变量进行赋值                                
                             %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
u=ones(c,n)/n;       %第一部分

m=ones(1,n);         %第二部分
l=ones(1,n);
r=ones(1,n);

M=ones(1,c);         %第三部分
L=ones(1,c);
R=ones(1,c);

for i=1:n             %第四部分
    m(i)=x(i,1);
    l(i)=x(i,2);
    r(i)=x(i,3);
end
for i=1:c             
    M(i)=x(i,1)-0.1;
    L(i)=x(i,2);
    R(i)=x(i,3);
end


                             %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
                             % 以下为定义变量部分：                                                                    
                             %    第五部分声明修改后的类中心的中心，左区间长，右区间长三个空间                             
                             %    第六部分计算优化后的的隶属度矩阵uu                                                                                  
                             %    第七部分计算修改后的类中心的中心，左区间长，右区间长，并进行赋值                    
                             %    第八部分对修改后的矩阵进行空间声明，同时进行计算赋值，其中用到的公式已经在论文中有提及  
                             %    第九部分将计算所得的类中心和左右区间长赋值给center变量，准备作为输出结果                
                             %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
LL=ones(1,c);
RR=ones(1,c);
uu=u;  
for j=1:n                    %第六部分
    for i=1:c
        dd(i)=1/(d([M(i),L(i),R(i)],[m(j),l(j),r(j)],a,b)^(2/(mm-1)));
    end
    for i=1:c
        uu(i,j)=1/(d([M(i),L(i),R(i)],[m(j),l(j),r(j)],a,b)^(2/(mm-1))*sum(dd));
    end
    dd=ones(1,c);
end

for i=1:c                   %第七部分      
    MM(i)=sum((uu(i,:).^mm).*(3*m-a*(l-L(i))+b*(r-R(i))))/(3*sum(uu(i,:).^mm));
    LL(i)=sum((uu(i,:).^mm).*(M(i)+a*l-m))/(3*sum(uu(i,:).^mm));
    RR(i)=sum(uu(i,:).^mm.*(m+a*r-M(i)))/(a*sum(uu(i,:).^mm));
end

       

center(:,1)=MM(:);         %第九部分
center(:,2)=LL(:);
center(:,3)=RR(:);


                             %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
                             %    第十部分，进入循环，循环条件为：u矩阵和uu矩阵中相应元素的差大于阈值e，则进入循环，将新计算的结果赋给对应的旧变量，重复第六部分到第九部分；否则退出循环                                                              
                             %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
while max(max(abs(u-uu)))>e    %第十部分
    u=uu;
    M(:)=MM(:);
    L(:)=LL(:);
    R(:)=RR(:);
    for i=1:c
    MM(i)=sum((uu(i,:).^mm).*(3*m-a*(l-L(i))+b*(r-R(i))))/(3*sum(uu(i,:).^mm));
    LL(i)=sum((uu(i,:).^mm).*(M(i)+a*l-m))/(3*sum(uu(i,:).^mm));
    RR(i)=sum(uu(i,:).^mm.*(m+a*r-M(i)))/(a*sum(uu(i,:).^mm));
    end
dd=ones(1,c);
uu=u;
for j=1:n
    for i=1:c
        dd(i)=1/d([MM(i),LL(i),RR(i)],[m(j),l(j),r(j)],a,b)^(2/(mm-1));
    end
    for i=1:c
        uu(i,j)=1/(d([MM(i),LL(i),RR(i)],[m(j),l(j),r(j)],a,b)^(2/(mm-1))*sum(dd));
    end
end
center(:,1)=MM(:);
center(:,2)=LL(:);
center(:,3)=RR(:);
end


                             %%%%%%%%%%%%%%%%%%%%%%%%%%%
                             %                 输出结果：第一个为类中心，第二个为隶属函数                     
                             %%%%%%%%%%%%%%%%%%%%%%%%%%%
fprintf('The centers of the %d clustering is:\n',c)
center
fprintf('The membership of the elements relating to the %d clusters is:\n',c)
uu=uu'
fprintf('The graph has shown the result of the clustering, in which the red point is the center of the clusters.\n')
fprintf('Attention: Because of the axis in the two graphs is not constrained!.\n')


                             %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
                             %           作图，上面一张图表示原始数据（这里的隶属函数都认为是三角关系）；第二张图为各类的模糊数据  
                             %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
subplot(2,1,1)
hold on
for i=1:n
    plot([m(i)-l(i),m(i),m(i)+r(i)],[0,1,0]);
end
hold off
title('The figure of the original data')
subplot(2,1,2)
hold on
for i=1:c
    plot([MM(i)-LL(i),MM(i),MM(i)+RR(i)],[0,1,0]);
end
title('The centers and spreads of the cluters')
hold off


end