# The most popular hierarchical clustering algorithm (divisive scheme)

My question: what is a "standard divisive hierarchical clustering algorithm".

I have a well-defined similarity matrix, and have already carried out a clustering (with spectral + genetic clustering algorithms), but it's quite complicated.

I would like to show that a run-of-the-mill divisive hierarchical clustering algorithm gives worse results (I have means of saying which results are better).

What's important: it MUST be (for reasons too political to explain) a divisive hierarchical algorithm, and it MUST use a similarity matrix (and not, for example, a distance matrix).

I would really appreciate any advice.

• run-of-the-mill hierarchical clustering algorithm Which algorithm? – ttnphns May 14 '15 at 6:03
• that's exactly my question - what is the most commonly (run-of-the-mill) used hierarchical clustering algorithm – ponadto May 14 '15 at 6:21
• Agglomerative one, not divisive. – ttnphns May 14 '15 at 6:27
• right, sorry, I should make that unambiguous. – ponadto May 14 '15 at 6:36
• Unambiguous? The question still mentions divisive. Do you indeed mean agglomerative? As far as I know all those hierarchical agglomerative algorithms work on distances. Your question sounds like you know the answer already. "I would like to show that x gives worse results than y". You will first have to check whether that is the case, right? But if that is the answer you want there will always be ways to get it, especially if there are political reasons. I'm really not impressed with this question. Is there somewhere a respectable quest for objective validation in there? – micans May 15 '15 at 10:05