由网友(半城烟沙半城雨)分享简介：我有一个（对称）矩阵 M 的再presents每一对节点之间的距离。例如，I have a (symmetric) matrix M that represents the distance between each pair of nodes. For example,A B C D E F...

我有一个（对称）矩阵 M 的再presents每一对节点之间的距离。例如，

I have a (symmetric) matrix M that represents the distance between each pair of nodes. For example,

    A   B   C   D   E   F   G   H   I   J   K   L
A   0  20  20  20  40  60  60  60 100 120 120 120
B  20   0  20  20  60  80  80  80 120 140 140 140
C  20  20   0  20  60  80  80  80 120 140 140 140
D  20  20  20   0  60  80  80  80 120 140 140 140
E  40  60  60  60   0  20  20  20  60  80  80  80
F  60  80  80  80  20   0  20  20  40  60  60  60
G  60  80  80  80  20  20   0  20  60  80  80  80
H  60  80  80  80  20  20  20   0  60  80  80  80
I 100 120 120 120  60  40  60  60   0  20  20  20
J 120 140 140 140  80  60  80  80  20   0  20  20
K 120 140 140 140  80  60  80  80  20  20   0  20
L 120 140 140 140  80  60  80  80  20  20  20   0

是否有任何方法来提取簇从 M （如果需要的话，群集数可以是固定的），使得每个集群包含在它们之间的小的距离的节点。在这个例子中，该集群将（A，B，C，D），（E，F，G，H）和（I，J，K，L）。

Is there any method to extract clusters from M (if needed, the number of clusters can be fixed), such that each cluster contains nodes with small distances between them. In the example, the clusters would be (A, B, C, D), (E, F, G, H) and (I, J, K, L).

多谢：）

推荐答案

分层聚类直接与距离矩阵，而不是的实际观测。如果你知道簇的数目，你就已经知道你停止准则（当有k个簇停止）。主要的窍门，这里将是选择一个合适的联动方式。此外，本文（PDF）给出了各类的优秀概述聚类方法。

Hierarchical clustering works directly with the distance matrix instead of the actual observations. If you know the number of clusters, you will already know your stopping criterion (stop when there are k clusters). The main trick here will be to choose an appropriate linkage method. Also, this paper(pdf) gives an excellent overview of all kinds of clustering methods.