由网友(天涯浪人)分享简介：我有一组对象的 {OBJ1，OBJ2，obj3，...，objn} 。我算过所有可能对的成对距离。的距离存储在 N *ñ矩阵 M ，与自我介绍是在 obji 和 objj 的距离。然后很自然地看到 M 是一个对称矩阵。现在我要执行无监督聚类对这些对象。经过一番搜索，我发现谱聚类可能是一个不错的选择，因为它涉及这样的pa...

我有一组对象的 {OBJ1，OBJ2，obj3，...，objn} 。我算过所有可能对的成对距离。的距离存储在 N *ñ矩阵 M ，与自我介绍是在 obji 和 objj 的距离。然后很自然地看到 M 是一个对称矩阵。

现在我要执行无监督聚类对这些对象。经过一番搜索，我发现谱聚类可能是一个不错的选择，因为它涉及这样的pairwise-距离的情况。

不过，经过仔细阅读其描述，我觉得我的情况不适合，因为它需要集群作为输入的数量。聚类前，不知簇的数目。它必须想出由算法在执行聚类，像DBSCAN

考虑到这些，请建议我一些适合我的情况下，聚类方法，其中

在成对的距离都可以使用。 簇的数目是未知的。 解决方案

有许多可能的聚类方法，其中没有一个可以被认为是最好的，一切都依赖于数据，一如既往的：

如果您想使用谱聚类，但不知道集群的手之前，我建议采取一看的自整定谱聚类或部分的方法确定集群 如果你考虑其它的算法，你可以尝试： DBSCAN 光学 密度-Link的聚类 分层聚类

I have a set of objects {obj1, obj2, obj3, ..., objn}. I have calculated the pairwise distances of all possible pairs. The distances are stored in a n*n matrix M, with Mij being the distance between obji and objj. Then it is natural to see M is a symmetric matrix.

Now I wish to perform unsupervised clustering to these objects. After some searching, I find Spectral Clustering may be a good candidate, since it deals with such pairwise-distance cases.

However, after carefully reading its description, I find it unsuitable in my case, as it requires the number of clusters as the input. Before clustering, I don't know the number of clusters. It has to be figured out by the algorithm while performing the clustering, like DBSCAN.

Considering these, please suggest me some clustering methods that fit my case, where

The pairwise distances are all available. The number of clusters is unknown.

解决方案

There are many possible clustering methods, and none of them can be considered "best", everything depends on the data, as always:

If you would like to use spectral clustering, but do not know the number of clusters before hand I suggest taking a look at the self-tuning spectral clustering or some methods of determining the number of clusters If you consider other algorithms you could try: DBSCAN OPTICS Density-Link-Clustering Hierarchical clustering