聚类-如何找到离聚类最近的 [英] Clustering - how to find the nearest to a cluster

问题描述

关于另一个问题的提示使我很困惑。

Hints I got as to a different question puzzled me quite a bit.

我做了一个练习，实际上是更大练习的一部分：

I got an exercise, actually part of a larger exercise:

1. 使用hclust（完成）聚类一些数据


2. 给出一个全新的向量，找出您要对哪个集群进行进来1就是最接近的。

根据练习，应该在很短的时间内完成。

According to the excercise, this should be done in quite short a time.

但是，几周后我不知道这是否可以完成，因为我从hclust那里真正得到的只是一棵树，而不是我想象的许多簇。

However, after weeks I am puzzled whether this can be done at all, as apparently all I really get from hclust is a tree - and not, as I assumed, a number of clusters.

我想我还不清楚：

例如，我要输入一个包含15个矩阵1x5向量，5倍（1 1 1 1 1），5倍（2 2 2 2 2）和5倍（3 3 3 3 3）。这应该给我三个大小分别为5的截然不同的群集，任何人都可以轻松地手动完成。有没有要使用的命令，以便我实际上可以从程序中找出hclust对象中有3个此类集群以及它们包含的集群？

Say, for instance, I feed hclust a matrix which consists of 15 1x5 Vectors, 5 times (1 1 1 1 1 ), 5 times (2 2 2 2 2) and 5 times (3 3 3 3 3). This should give me three quite distinct clusters of size 5, anyone can easily do that by hand. Is there a command to use so that I can actually find out from the program that there are 3 such clusters in my hclust-object and what they contain?

推荐答案

与k均值相比，hclust发现的簇可以具有任意形状。

In contrast to k-means, clusters found by hclust can be of arbitrary shape.

因此，到最近的簇中心的距离并不总是

The distance to the nearest cluster center therefore is not always meaningful.

进行1个最近邻居样式分配可能更好。

Doing a 1 nearest neighbor style assignment probably is better.

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