基于MATLAB的KMEANS聚类程序Word文档格式.docx
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[n,d]=size（x）;
bn=round（n/k*rand）;
%第一个随机数在前1/K的范围内
nc=[x（bn,:
）;
x（2*bn,:
x（3*bn,:
x（4*bn,:
）];
%初始聚类中心
[cid,nr,centers]=kmeans（x,k,nc）%调用kmeans函数
fori=1:
150,
ifcid（i）==1,
plot（x（i,1）,x（i,2）,'
r*'
）%显示第一类
holdon
else
ifcid（i）==2,
b*'
）%显示第二类
else
ifcid（i）==3,
plot（x（i,1）,x（i,2）,'
g*'
）%显示第三类
holdon
ifcid（i）==4,
k*'
）%显示第四类
end
end
strt=['
红色*为第一类；
蓝色*为第二类；
绿色*为第三类；
黑色*为第四类'
];
text（-4,-3.6,strt）;
函数如下:
%BasicKMeans.m主类
function[cid,nr,centers]=kmeans（x,k,nc）
%设置cid为分类结果显示矩阵
cid=zeros（1,n）;
%Makethisdifferenttogettheloopstarted.
oldcid=ones（1,n）;
%Thenumberineachcluster.
nr=zeros（1,k）;
%Setupmaximumnumberofiterations.
maxgn=100;
iter=1;
whileiter<
maxgn
%计算每个数据到聚类中心的距离
fori=1:
n
dist=sum（（repmat（x（i,:
）,k,1）-nc）.^2,2）;
[m,ind]=min（dist）;
%将当前聚类结果存入cid中
cid（i）=ind;
k
%找到每一类的所有数据，计算他们的平均值，作为下次计算的聚类中心
ind=find（cid==i）;
nc（i,:
）=mean（x（ind,:
））;
%统计每一类的数据个数
nr（i）=length（ind）;
iter=iter+1;
%Nowcheckeachobservationtoseeiftheerrorcanbeminimizedsomemore.
%Loopthroughallpoints.
maxiter=2;
move=1;
maxiter&
move~=0
move=0;
%对所有的数据进行再次判断，寻求最佳聚类结果
r=cid（i）;
%将当前数据属于的类给r
dadj=nr./（nr+1）.*dist'
;
%计算调整后的距离
[m,ind]=min（dadj）;
%早到该数据距哪个聚类中心最近
ifind~=r
%如果不等则聚类中心移动
%将新的聚类结果送给cid
ic=find（cid==ind）;
%重新计算调整当前类别的聚类中心
nc（ind,:
）=mean（x（ic,:
iter=iter+1;
centers=nc;
ifmove==0
disp（'
Nopointsweremovedaftertheinitialclusteringprocedure.'
）
Somepointsweremovedaftertheinitialclusteringprocedure.'
end分类效果图
14_7774_ecd62647e667583.jpg
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