Pruning dendrogram in scipy (hierarchical clustering)
hierarchical-clustering, python
Solution
One of the dictionary data-structures returned by scipy.cluster.hierarchy.dendrogram has the key `ivl`, that the documentation describes as:
a list of labels corresponding to the leaf nodes
You can supply custom labels (using `labels=<array of lables>`) as input to the dendrogram function but by default, they are just indices of the original observation. By comparing the original labels/indices and `Z1['ivl']`, you can determine what the original entries were.
Problem
I have a distance matrix with about 5000 entries, and use scipy's hierarchical clustering methods to cluster the matrix. The code I use for this is the following snippet: ``` Y = fastcluster.linkage(D, method='centroid') # D-distance matrix Z1 = sch.dendrogram(Y,truncate_mode='level', p=7,show_contracted=True) ``` Since the dendrogram will become rather dense with all this data, I use the truncate_mode to prune it a bit. All of this works, but I wonder how I can find out which of the original 5000 entries belong to a particular branch in the dendrogram. I tried using ``` leaves = sch.leaves_list(Y) ``` to get a list of leaves, but this uses the linkage output as indata, and while I can see the correspondence between the pruned dendrogram and the leaves-list, it becomes a bit cumbersome to map original entries manually to the dendrogram. To summarize: Is there a way of listing all the original entries in the distance matrix that belongs to a branch in a pruned dendrogram? Or are there other methods of doing this that I am not aware of. Thanks