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
a
ithms, 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?
>
ature? Are there
> alternatives? (I've not read up yet.)
>
> Thanks,
>
> Joel
>
> On 17 May 2015 at 06:43, Sam Schetterer wrote:
>
>> Andreas,
>>
>> There isn't necessarily a linkage function defined, at least in the
>> sense of agglome
