K-means算法的matlab程序（初步）.html

<h1 style="text-align: center;">K-means算法的matlab程序</h1>
<p><span style="font-size: 16px;">在<a title="聚类&mdash;&mdash;K-means - 凯鲁嘎吉 - 博客园 " href="https://www.cnblogs.com/kailugaji/p/9648369.html" target="_blank">https://www.cnblogs.com/kailugaji/p/9648369.html</a>&nbsp;文章中已经介绍了K-means算法，现在用matlab程序实现它。</span></p>
<p><span style="font-size: 16px;">作者：凯鲁嘎吉 - 博客园&nbsp;http://www.cnblogs.com/kailugaji/</span></p>
<h2><span style="font-size: 18px;">1.采用iris数据库</span></h2>
<p><span style="font-size: 16px;">iris_data.txt</span></p>
<div class="cnblogs_code" onclick="cnblogs_code_show('ab3ba6f2-3f92-4b5c-b903-52c466fb93bc')"><img id="code_img_closed_ab3ba6f2-3f92-4b5c-b903-52c466fb93bc" class="code_img_closed" src="http://images.cnblogs.com/OutliningIndicators/ContractedBlock.gif" alt="" /><img id="code_img_opened_ab3ba6f2-3f92-4b5c-b903-52c466fb93bc" class="code_img_opened" style="display: none;" onclick="cnblogs_code_hide('ab3ba6f2-3f92-4b5c-b903-52c466fb93bc',event)" src="http://images.cnblogs.com/OutliningIndicators/ExpandedBlockStart.gif" alt="" />
<div id="cnblogs_code_open_ab3ba6f2-3f92-4b5c-b903-52c466fb93bc" class="cnblogs_code_hide">
<pre>5.1    3.5    1.4    0.2
4.9    3    1.4    0.2
4.7    3.2    1.3    0.2
4.6    3.1    1.5    0.2
5    3.6    1.4    0.2
5.4    3.9    1.7    0.4
4.6    3.4    1.4    0.3
5    3.4    1.5    0.2
4.4    2.9    1.4    0.2
4.9    3.1    1.5    0.1
5.4    3.7    1.5    0.2
4.8    3.4    1.6    0.2
4.8    3    1.4    0.1
4.3    3    1.1    0.1
5.8    4    1.2    0.2
5.7    4.4    1.5    0.4
5.4    3.9    1.3    0.4
5.1    3.5    1.4    0.3
5.7    3.8    1.7    0.3
5.1    3.8    1.5    0.3
5.4    3.4    1.7    0.2
5.1    3.7    1.5    0.4
4.6    3.6    1    0.2
5.1    3.3    1.7    0.5
4.8    3.4    1.9    0.2
5    3    1.6    0.2
5    3.4    1.6    0.4
5.2    3.5    1.5    0.2
5.2    3.4    1.4    0.2
4.7    3.2    1.6    0.2
4.8    3.1    1.6    0.2
5.4    3.4    1.5    0.4
5.2    4.1    1.5    0.1
5.5    4.2    1.4    0.2
4.9    3.1    1.5    0.2
5    3.2    1.2    0.2
5.5    3.5    1.3    0.2
4.9    3.6    1.4    0.1
4.4    3    1.3    0.2
5.1    3.4    1.5    0.2
5    3.5    1.3    0.3
4.5    2.3    1.3    0.3
4.4    3.2    1.3    0.2
5    3.5    1.6    0.6
5.1    3.8    1.9    0.4
4.8    3    1.4    0.3
5.1    3.8    1.6    0.2
4.6    3.2    1.4    0.2
5.3    3.7    1.5    0.2
5    3.3    1.4    0.2
7    3.2    4.7    1.4
6.4    3.2    4.5    1.5
6.9    3.1    4.9    1.5
5.5    2.3    4    1.3
6.5    2.8    4.6    1.5
5.7    2.8    4.5    1.3
6.3    3.3    4.7    1.6
4.9    2.4    3.3    1
6.6    2.9    4.6    1.3
5.2    2.7    3.9    1.4
5    2    3.5    1
5.9    3    4.2    1.5
6    2.2    4    1
6.1    2.9    4.7    1.4
5.6    2.9    3.6    1.3
6.7    3.1    4.4    1.4
5.6    3    4.5    1.5
5.8    2.7    4.1    1
6.2    2.2    4.5    1.5
5.6    2.5    3.9    1.1
5.9    3.2    4.8    1.8
6.1    2.8    4    1.3
6.3    2.5    4.9    1.5
6.1    2.8    4.7    1.2
6.4    2.9    4.3    1.3
6.6    3    4.4    1.4
6.8    2.8    4.8    1.4
6.7    3    5    1.7
6    2.9    4.5    1.5
5.7    2.6    3.5    1
5.5    2.4    3.8    1.1
5.5    2.4    3.7    1
5.8    2.7    3.9    1.2
6    2.7    5.1    1.6
5.4    3    4.5    1.5
6    3.4    4.5    1.6
6.7    3.1    4.7    1.5
6.3    2.3    4.4    1.3
5.6    3    4.1    1.3
5.5    2.5    4    1.3
5.5    2.6    4.4    1.2
6.1    3    4.6    1.4
5.8    2.6    4    1.2
5    2.3    3.3    1
5.6    2.7    4.2    1.3
5.7    3    4.2    1.2
5.7    2.9    4.2    1.3
6.2    2.9    4.3    1.3
5.1    2.5    3    1.1
5.7    2.8    4.1    1.3
6.3    3.3    6    2.5
5.8    2.7    5.1    1.9
7.1    3    5.9    2.1
6.3    2.9    5.6    1.8
6.5    3    5.8    2.2
7.6    3    6.6    2.1
4.9    2.5    4.5    1.7
7.3    2.9    6.3    1.8
6.7    2.5    5.8    1.8
7.2    3.6    6.1    2.5
6.5    3.2    5.1    2
6.4    2.7    5.3    1.9
6.8    3    5.5    2.1
5.7    2.5    5    2
5.8    2.8    5.1    2.4
6.4    3.2    5.3    2.3
6.5    3    5.5    1.8
7.7    3.8    6.7    2.2
7.7    2.6    6.9    2.3
6    2.2    5    1.5
6.9    3.2    5.7    2.3
5.6    2.8    4.9    2
7.7    2.8    6.7    2
6.3    2.7    4.9    1.8
6.7    3.3    5.7    2.1
7.2    3.2    6    1.8
6.2    2.8    4.8    1.8
6.1    3    4.9    1.8
6.4    2.8    5.6    2.1
7.2    3    5.8    1.6
7.4    2.8    6.1    1.9
7.9    3.8    6.4    2
6.4    2.8    5.6    2.2
6.3    2.8    5.1    1.5
6.1    2.6    5.6    1.4
7.7    3    6.1    2.3
6.3    3.4    5.6    2.4
6.4    3.1    5.5    1.8
6    3    4.8    1.8
6.9    3.1    5.4    2.1
6.7    3.1    5.6    2.4
6.9    3.1    5.1    2.3
5.8    2.7    5.1    1.9
6.8    3.2    5.9    2.3
6.7    3.3    5.7    2.5
6.7    3    5.2    2.3
6.3    2.5    5    1.9
6.5    3    5.2    2
6.2    3.4    5.4    2.3
5.9    3    5.1    1.8</pre>
</div>
<span class="cnblogs_code_collapse">View Code</span></div>
<h2><span style="font-size: 18px;">2.matlab源程序</span></h2>
<p><span style="font-size: 16px;">My_Kmeans.m</span></p>
<div class="cnblogs_Highlighter">
<pre class="brush:matlab;gutter:true;">function label_1=My_Kmeans(K)
%输入K：聚类数
%输出：label_1:聚的类, para_miu_new:聚类中心&mu;
format long
eps=1e-5;  %定义迭代终止条件的eps
data=dlmread('E:\www.cnblogs.comkailugaji\data\iris\iris_data.txt');
%----------------------------------------------------------------------------------------------------
%对data做最大-最小归一化处理
[data_num,~]=size(data);
X=(data-ones(data_num,1)*min(data))./(ones(data_num,1)*(max(data)-min(data)));
[X_num,~]=size(X);
%----------------------------------------------------------------------------------------------------
%随机初始化K个聚类中心
rand_array=randperm(X_num);  %产生1~X_num之间整数的随机排列
para_miu_new=X(rand_array(1:K),:);  %随机排列取前K个数，在X矩阵中取这K行作为初始聚类中心
responsivity=zeros(X_num,K);
%----------------------------------------------------------------------------------------------------
%K-means算法
while true
    para_miu=para_miu_new;  %上一步的聚类中心
    %欧氏距离，计算（X-para_miu）^2=X^2+para_miu^2-2*X*para_miu'，矩阵大小为X_num*K
    distant=repmat(sum(X.*X,2),1,K)+repmat(sum(para_miu.*para_miu,2)',X_num,1)-2*X*para_miu';
    %返回distant每行最小值所在的下标
    [~,label_1]=min(distant,[],2);
    %构建隶属度矩阵X_num*K
    for i=1:X_num
        for j=1:K
            responsivity(i,j)=isequal(j,label_1(i));
        end
    end
    R_k=sum(responsivity,1);  %分母,第k类的个数,1*k的矩阵
    para_miu_new=diag(1./R_k)*responsivity'*X;  %更新参数miu(聚类中心)
    if norm(para_miu_new-para_miu)&lt;=eps
        break;
    end
end</pre>
</div>
<h2><span style="font-size: 18px;">3.结果</span></h2>
<div class="cnblogs_Highlighter">
<pre class="brush:matlab;collapse:true;;gutter:true;">&gt;&gt; label_1=My_Kmeans(3)

label_1 =

     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     3
     2
     2
     2
     1
     1
     1
     2
     1
     1
     1
     1
     1
     1
     1
     1
     2
     1
     1
     1
     1
     2
     1
     1
     1
     1
     1
     2
     2
     1
     1
     1
     1
     1
     1
     1
     2
     2
     1
     1
     1
     1
     1
     1
     1
     1
     1
     1
     1
     1
     1
     2
     1
     2
     2
     2
     2
     1
     2
     2
     2
     2
     2
     2
     1
     2
     2
     2
     2
     2
     1
     2
     1
     2
     2
     2
     2
     2
     2
     2
     2
     2
     2
     2
     1
     1
     2
     2
     2
     2
     2
     2
     2
     1
     2
     2
     2
     2
     2
     2
     2</pre>
</div>
<h2><span style="font-size: 18px;">4.注意</span></h2>
<p>&nbsp; &nbsp; <span style="font-size: 16px;">由于初始化聚类中心是随机的，所以每次出现的结果并不一样，如果答案与上述不一致，很正常，可以设置迭代次数，取平均值。如有不对之处，望指正。</span></p>