In my experience it is not very helpful to talk about agglomerative vs
divisive algorithms, as that is often more of an implementation detail.
Single-link agglomerative clustering for example is often implemented by
computing the spanning tree and then cutting it.
So it is more of a question what your linkage criterion is.
What criteria are you thinking of?
On 05/15/2015 11:27 AM, Sam Schetterer wrote:
Is there any interest in adding divisive hierarchical clustering
algorithms to scikit-learn? They are useful for document clustering
[1] and biostats [2], and can have much better time complexity than
agglomerative approaches ([1], can run in ~O(n*log(k)), where k is the
number of clusters). This algorithm also allows one to do learning
that only includes information from a certain sub-cluster, like
rebuilding a tf-idf corpus at each hierarchy level, and allows for
more sophisticated stopping criteria than number of clusters.
There's a rough (and incomplete) implementation at
https://github.com/schets/scikit-learn/blob/splitting-hierarchical-clustering/sklearn/cluster/splitting_hierarchical.py,
that allows for a variety of divisive algorithms to be readily used
including the algorithms from both [1] and [2].
I've also had great success using the generated tree hierarchies as a
sort of template for aggregating data / dynamic clustering, although
I'm not sure how well that would fit with the standard scikit-learn
api or how common my use case is.
1.
http://www.researchgate.net/profile/Vipin_Kumar26/publication/2628533_A_Comparison_of_Document_Clustering_Techniques/links/00b4951675a8a82fcc000000.pdf
2.
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0002247
Best,
Sam
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