Mean Shift聚类算法机器学习Mean Shift(2)

end

data=x';

% plot(data(:,1),data(:,2),'.')

%mean shift 算法

[m,n]=size(data);

index=1:m;

radius=0.75;

stopthresh=1e-3*radius;

visitflag=zeros(m,1);%标记是否被访问

count=[];

clustern=0;

clustercenter=[];

hold on;

while length(index)>0

cn=ceil((length(index)-1e-6)*rand);%随机选择一个未被标记的点，作为圆心，进行均值漂移迭代

center=data(index(cn),:);

this_class=zeros(m,1);%统计漂移过程中，每个点的访问频率

%步骤2、3、4、5

while 1

%计算球半径内的点集

dis=sum((repmat(center,m,1)-data).^2,2);

radius2=radius*radius;

innerS=find(dis<radius*radius);

visitflag(innerS)=1;%在均值漂移过程中，记录已经被访问过得点

this_class(innerS)=this_class(innerS)+1;

%根据漂移公式，计算新的圆心位置

newcenter=zeros(1,2);

% newcenter= mean(data(innerS,:),1);

sumweight=0;

for i=1:length(innerS)

w=exp(dis(innerS(i))/(radius*radius));

sumweight=w+sumweight;

newcenter=newcenter+w*data(innerS(i),:);

end

newcenter=newcenter./sumweight;

if norm(newcenter-center) <stopthresh%计算漂移距离，如果漂移距离小于阈值，那么停止漂移

break;

end

center=newcenter;

plot(center(1),center(2),'*y');

end

%步骤6 判断是否需要合并，如果不需要则增加聚类个数1个

mergewith=0;

for i=1:clustern

betw=norm(center-clustercenter(i,:));

if betw<radius/2

mergewith=i;

break;

end

if mergewith==0 %不需要合并

clustern=clustern+1;

clustercenter(clustern,:)=center;

count(:,clustern)=this_class;

else %合并

clustercenter(mergewith,:)=0.5*(clustercenter(mergewith,:)+center);

count(:,mergewith)=count(:,mergewith)+this_class;

end

%重新统计未被访问过的点论文网

index=find(visitflag==0);

end%结束所有数据点访问

%绘制分类结果

for i=1:m

[value index]=max(count(i,:));

Idx(i)=index;

end

figure(2);

hold on;

for i=1:m

if Idx(i)==1;

plot(data(i,1),data(i,2),'.y'); Mean Shift聚类算法机器学习Mean Shift(2):http://www.youerw.com/fanwen/lunwen_61939.html