kmeansk均值聚类

1、K-means聚类算法采用的是将N*P的矩阵X划分为K个类，使得类内对象之间的距离最小,而类之间的距离最大。matlab自带Kmeans函数使用方法：Idx=Kmeans(X,K) Idx,C=Kmeans(X,K) Idx,C,sumD=Kmeans(X,K)Idx,C,sumD,D=Kmeans(X,K)=Kmeans(,Paraml,Val1,Param2,Val2,)各输入输 出参数介绍：X N*P的数据矩阵K表示将X划分为几类，为整数Idx N*1的向量，存储的是每个点的聚类标号C K*P的矩阵，存储的是K个聚类质心位置sumD 1*K的和向量，存储的是类间所有点与该类质心点距 离之

2、和D N*K的矩阵，存储的是每个点与所有质心的距离=Kmeans(,Param1,Val1,Param2,Val2,)这其中的参数Param1、Param2等，主要可以设置为如下：1.Distance(距离)测度sqEuclide a n欧式距离(默认时，采用此距离方式) cityb lock绝度误差和，又称：L1cosine针对向量KmeansK均值聚类correlation针对有时序关系的值Hamming只针对二进制数据2.Start初始质心位置选择方法)sample从X中随机选取K个质心点uniform根据X的分布范围均匀的随机生成K个质心cluster初始台聚类阶段随机选择10%的X的

3、子样本(此方法初始使用sample方法)matrix提供一K*P的矩阵，作为初始质心位置集合3.Replicates(聚类重复次数)整数%图片处理的一个实例：clcclear allclose all video=mmreader(che2.AVI);I1=read(video,589); %I2=read(video,590);% I1=rgb2gray(I1);% I2=rgb2gray(I2);he=I1;figure,imshow(he), title(Your Image);text(size(he,2),size(he,1)+15,.DIA-SA07006042-ZhangM,US

4、TC, .FontSize,7,HorizontalAlignment,right);%Step 2: Convert image from RGB color space to L*a*b* color spacecform = makecform(srgb2lab);lab_he = applycform(he,cform);%lab_he=rgb2hsi(he);%Step 3: Classify the colors in a*b* space using K-means clusteringab = double(lab_he(:,:,2:3);nrows = size(ab,1);

5、ncols = size(ab,2);ab = reshape(ab,nrows*ncols,2); %把ab变成nrows*ncols行2列N=3; %How many color there are in the image. nColors = N;% repeat the clustering 3 times to avoid local minimacluster_idx,cluster_center =kmeans(ab,nColors,distance,sqEuclidean, .Replicates,N);m=4;%Step 4: Label every pixel in th

6、e image using the results from kmeanspixel_labels = reshape(cluster_idx,nrows,ncols);figure,imshow(pixel_labels,), title(image labeled by cluster index);%Step 5: Create images that segment the H&E image by color.segmented_images = cell(1,N); rgb_label = repmat(pixel_labels,1 1 N);for k = 1:nColorscolor = he; color(rgb_label = k) = 0;segmented_imagesk = color;end figure, subplot(131),imshow(segmented_images1), title(objects incluster 1);subplot(132),imshow(segmented_im