Machine Learning by Andrew Ng --- K-means

最新推荐文章于 2019-05-17 20:26:46 发布

原创最新推荐文章于 2019-05-17 20:26:46 发布 · 1.1k 阅读

0 ·

CC 4.0 BY-SA版权

文章标签：

#machine

ML 专栏收录该内容

26 篇文章

订阅专栏

本文深入探讨了K均值聚类算法的工作原理及应用，包括算法的两个核心步骤：将每个实例分配到最近的质心，并计算每个质心的平均值，更新质心位置。同时提供了算法实现的代码片段。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

K-means is a kind of cluster algorithm, It allows us to find characteristics in the No characteristic data.
The two important steps of K-means : One is that clustering every example to its right centroid . Another is that computing every example of each centroid’s average,then move the correspond cenroid to the average point.

Here are the whole steps of K-mean:

random initialize K(it is better to choose K from the exit examples).
repeat{ 1.clustering every example to its right centroid 2. computing every example of each centroid’s average,then move the correspond cenroid to the average point. }

Something more important already mentioned in the lecture notes.

The solution (U should do it by yourself,i am just recording so i can review it future)

findClosestCentroids.m

 

minvec=zeros(K,1); 

for index = 1:size(X,1) 

    for i = 1:K 

        minvec( i , 1 ) = sum( ( X(index,:)-centroids(i,:) ).^2 ) ; 

    end  

    [m , n] = min( minvec ); 

    idx(index , 1) = n; 

end

computeCentroids.m

for index = 1:K 

    m1 = find(idx==index); 

    centroids(index,:) = ( sum(X(m1,:)) )/size(m1,1); 

end