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 # 第 10 章 K-Means（K-均值）聚类算法
 
+![K-Means（K-均值）聚类算法_首页](/images/10.KMeans/K-Means_首页.jpg)
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 ## K-Means 算法
 聚类是一种无监督的学习, 它将相似的对象归到一个簇中, 将不相似对象归到不同簇中.  
 相似这一概念取决于所选择的相似度计算方法.  
@@ -195,4 +197,8 @@ def biKMeans(dataSet, k, distMeas=distEclud):
 
 上述函数可以运行多次，聚类会收敛到全局最小值，而原始的 kMeans() 函数偶尔会陷入局部最小值。  
 运行参考结果如下:  
-![二分 K-Means 运行结果1](../images/10.KMeans/apachecn-bikmeans-run-result-1.jpg)
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+![二分 K-Means 运行结果1](../images/10.KMeans/apachecn-bikmeans-run-result-1.jpg)
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+* **作者：[那伊抹微笑](http://www.apache.wiki/display/~xuxin)**
+* [GitHub地址](https://github.com/apachecn/MachineLearning): <https://github.com/apachecn/MachineLearning>
+* **版权声明：欢迎转载学习 => 请标注信息来源于 [ApacheCN](http://www.apachecn.org/)**
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