# AGNES层次聚类

试图从不同层次对数据集进行划分，从而形成树形的聚类结构。自底向上的聚会策略，先将每个样本看做一个聚类簇（m个样本就是m个簇），然后每次运行找出距离最近的两个簇进行合并，不断重复，直到达到设定的k值。注意的是，簇的距离计算的定义：

按照这3种min、max、avg计算距离，AGNES算法被称为“单链接”、“全链接”、“均链接”

算法流程（《机器学习》周志华，p215）：

> 6,7行计算每个样本间相互距离；13-15合并 i *和 j* 两号的簇并入 i *内，j* 以后的簇全部更新编号;
>
> 17行，删除所有与 j\* 号簇有关的距离信息;
>
> 19-20行，（只需要）更新第 i\* 号簇与其他簇的距离;

![img](https://img-blog.csdnimg.cn/20190302115216972.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ppYW5nNDI1Nzc2MDI0,size_16,color_FFFFFF,t_70)

图例：

![img](https://img-blog.csdnimg.cn/20190302120316129.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ppYW5nNDI1Nzc2MDI0,size_16,color_FFFFFF,t_70)

sklearn中没有实现DBSCAN层次聚类的算法，不过这个算法实际操作简单，而且下面有一个更实用的层次聚类算法在sklearn中有实现，因此这个可以看看作为思想


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