2022年模糊聚类函数的matlab代码 .pdf

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1、模糊聚类函数的matlab 代码= function R = coesim1(datin,m) % 聚类分析相关系数计算公式1 数量积% R = coesim1(datin，C) % m 是乘积和最大值的一个参数，取决于输入数据datin error(nargchk(2,2, nargin) N M = size(datin); R=zeros(N,N); for i = 1:N for j = 1:N for k=1:M R(i,j) = R(i,j) + (datin(i,k)*datin(j,k)/m; end end R(i,i)=1; end = function R = coesi

2、m2(datin) % 聚类分析相关系数计算公式2 夹角余弦% R = coesim2(datin) error(nargchk(1,1, nargin) N M = size(datin); R=zeros(N,N); t=zeros(1,3); for i = 1:N for j = 1:N for k=1:M t(1)=t(1)+datin(i,k)*datin(j,k); 名师资料总结 - - -精品资料欢迎下载 - - - - - - - - - - - - - - - - - - 名师精心整理 - - - - - - - 第 1 页，共 8 页 - - - - - - - - -

3、t(2)=t(2)+datin(i,k)2; t(3)=t(3)+datin(j,k)2; end R(i,j) = abs(t(1)/sqrt(t(2)*t(3); t=zeros(1,3); end end = function R = coesim3(datin) % 聚类分析相关系数计算公式3 相关系数% R = coesim3(datin) error(nargchk(1,1, nargin) N M = size(datin); R=zeros(N,N); datinmean = mean(datin); t=zeros(1,3); for i = 1:N for j = 1:N

4、for k=1:M t(1)=t(1)+abs(datin(i,k)-datinmean(i)*(datin(j,k)-datinmean(j); t(2)=t(2)+(datin(i,k)-datinmean(i)2; t(3)=t(3)+(datin(j,k)-datinmean(j)2; end R(i,j) = t(1)/sqrt(t(2)*t(3); t=zeros(1,3); end end = 名师资料总结 - - -精品资料欢迎下载 - - - - - - - - - - - - - - - - - - 名师精心整理 - - - - - - - 第 2 页，共 8 页 - -

5、- - - - - - - function R = coesim5(datin) % 聚类分析相关系数计算公式5 非参数法% R = coesim5(datin) error(nargchk(1,1, nargin) N M = size(datin); R=zeros(N,N); datinmean = mean(datin); t=zeros(1,2); for i = 1:N for j = 1:N Ti = zeros(1,M); Tj = zeros(1,M); Ti=datin(i,:) - datinmean(i); Tj=datin(j,:) - datinmean(j);

6、t(1)=length(find(Ti.*Tj0); t(2)=length(find(Ti.*Tj0); R(i,j)=abs(t(1)-t(2)/(t(1)+t(2); end end = function R = coesim6(datin) % 聚类分析相关系数计算公式6 最大最小法% R = coesim6(datin) error(nargchk(1,1, nargin) N M = size(datin); 名师资料总结 - - -精品资料欢迎下载 - - - - - - - - - - - - - - - - - - 名师精心整理 - - - - - - - 第 3 页，共 8

7、 页 - - - - - - - - - R=zeros(N,N); for i = 1:N for j = 1:N R(i,j)=sum(min(datin(i,:),datin(j,:)/. sum(max(datin(i,:),datin(j,:); end end = function R = coesim7(datin) % 聚类分析相关系数计算公式7 算术平均最小法% R = coesim7(datin) error(nargchk(1,1, nargin) N M = size(datin); R=zeros(N,N); for i = 1:N for j = 1:N R(i,j

8、)=sum(min(datin(i,:),datin(j,:)/. (sum(sum(datin(i,:);datin(j,:)/2); end end = function R = coesim9(datin) % 聚类分析相关系数计算公式9 绝对值指数法% R = coesim9(datin) error(nargchk(1,1, nargin) N M = size(datin); R=zeros(N,N); for i = 1:N for j = 1:N R(i,j)=exp(-sum(abs(datin(i,:)-datin(j,:); end 名师资料总结 - - -精品资料欢迎下

9、载 - - - - - - - - - - - - - - - - - - 名师精心整理 - - - - - - - 第 4 页，共 8 页 - - - - - - - - - end = function R = coesim11(datin,C) % 聚类分析相关系数计算公式11 绝对值减数法% R = coesim11(datin) % r(i,j) = 1 , when i=j; % = 1-C*(M/k=1)|Xik - Xjk| , when j != i; error(nargchk(2,2, nargin) if(C1),error(C must between 0 and 1

10、);end N M = size(datin); R=ones(N,N); for i = 1:N for j = 1:N for k=1:M R(i,j) = R(i,j) - C*abs(datin(i,k)-datin(j,k); end end end = function R = coesimhm(datin) % 聚类分析相关系数计算公式 Hamming距离法% R = coesimhm(datin) error(nargchk(1,1, nargin) 名师资料总结 - - -精品资料欢迎下载 - - - - - - - - - - - - - - - - - - 名师精心整理

11、- - - - - - - 第 5 页，共 8 页 - - - - - - - - - N M = size(datin); R=zeros(N,N); max=0; for i = 1:N for j = 1:N R(i,j)=sum(abs(datin(i,:)-datin(j,:); if maxR(i,j) max=R(i,j); end end end for i = 1:N for j = 1:N R(i,j)=1-R(i,j)/max; end end = function R = coesimeu(datin) % 聚类分析相关系数计算公式 Euclid 欧氏距离法% R =

12、coesimeu(datin) error(nargchk(1,1, nargin) N M = size(datin); R=zeros(N,N); max=0; for i = 1:N for j = 1:N R(i,j)=sqrt(sum(datin(i,:)-datin(j,:).2); if max1 for i = 1:N for j = 1:N RP(i,j) = max(min(RP(i,:);datin(:,j); end end P=P-1; end = function RK K = fuzequmat(R) % Fuzzy equivalent matrix 求模糊等价

13、矩阵% RK K = fuzequmat(R) % RK 即求得的等价矩阵，K 为自乘的次数，R 为相似矩阵error(nargchk(1,1, nargin); K = 0; RK = R; while all(all(RK = rpower(RK,2) RK = rpower(RK,2); 名师资料总结 - - -精品资料欢迎下载 - - - - - - - - - - - - - - - - - - 名师精心整理 - - - - - - - 第 7 页，共 8 页 - - - - - - - - - K = K + 1; end K = 2*K; = 在 R*上对 U 分类，直接利用floor 函数R* = floor(R + (1 - )名师资料总结 - - -精品资料欢迎下载 - - - - - - - - - - - - - - - - - - 名师精心整理 - - - - - - - 第 8 页，共 8 页 - - - - - - - - -