I'm trying to build a dendrogram using the `children_`

attribute provided by `AgglomerativeClustering`

, but so far I'm out of luck. I can't use `scipy.cluster`

since agglomerative clustering provided in `scipy`

lacks some options that are important to me (such as the option to specify the amount of clusters). I would be really grateful for a any advice out there.

```
import sklearn.cluster
clstr = cluster.AgglomerativeClustering(n_clusters=2)
clusterer.children_
```

## Best Solution

Here is a simple function for taking a hierarchical clustering model from sklearn and plotting it using the scipy

`dendrogram`

function. Seems like graphing functions are often not directly supported in sklearn. You can find an interesting discussion of that related to the pull request for this`plot_dendrogram`

code snippet here.I'd clarify that the use case you describe (defining number of clusters) is available in scipy: after you've performed the hierarchical clustering using scipy's

`linkage`

you can cut the hierarchy to whatever number of clusters you want using`fcluster`

with number of clusters specified in the`t`

argument and`criterion='maxclust'`

argument.