Yes, the evidence accumulation approach is quite simple yet popular, and
can be implemented on current methods in sklearn. However, I have a doubt
about the current implementation of the agglomerative hierarchical
clustering. Although it could be implemented differently, I would need to
get the final partition by inspecting the tree and specifying the height of
the dendrogram, not the number of clusters (I see there is an open issue
<https://github.com/scikit-learn/scikit-learn/issues/3796> about this).
This way, it is not necessary, according to this approach, to specify the
number of clusters in advance.
Ronnie, there are many applications of these methods in a wide range of
areas, like bioinformatics, businesses and software engineering, among
others. You can look for "consensus clustering", "cluster ensemble",
"clustering aggregation". There are good accuracy/performance reports (in
general, an ensemble method outperforms individual members), as well as
articles
<http://www.nature.com/srep/2014/140827/srep06207/full/srep06207.html> that
doubt about its benefits (although this one does not seem to take into
account the diversity of the input ensemble, which is essential to get good
results).
Regarding GSoC, I have never participated before. I was reading it is a
full job (40 hours week). Unfortunately, I can't do that now, so I think it
would be better to contribute outside the program. Sorry, I had a wrong
idea of it and I should have read before.
Regards,
Milton.
2015-02-22 19:02 GMT-03:00 Andy <t3k...@gmail.com>:
> the paper is quite well cited (500):
> http://scholar.google.com/scholar?q=Combining%20multiple%20clusterings%20using%20evidence%20accumulation&btnG=Search&as_sdt=800000000001&as_sdtp=on
>
> I thought the idea was to add (some of) the ensemble methods described in
> the paper, which are meta-algorithms that could build on any of the methods
> we already have,
> as far as I understand.
>
>
>
> On 02/18/2015 04:06 AM, Ronnie Ghose wrote:
>
> is there clear use for this clustering method and a sizable number of
> citations and obviously performance/accuracy/something benefits that
> warrant the time & maitenance cost then?
>
> Also by that @andreas, I'm not seeing a list of clustering methods to be
> added, right now it seems unbounded - i don't like unbounded scopes, they
> dont make for good project topics
>
>
>
> On Wed, Feb 18, 2015 at 5:22 AM, Gael Varoquaux <
> gael.varoqu...@normalesup.org> wrote:
>
>> On Tue, Feb 17, 2015 at 04:42:11PM -0800, Andy wrote:
>> > On 02/13/2015 07:08 AM, Ronnie Ghose wrote:
>> > > -1 we would have to build in support for more clustering methods
>> > > ,sounds like a not-very-standalone proj
>> > Why? We already have a bunch, right?
>>
>> I agree with Andreas that any addition should be motivated: the new
>> clustering method should bring something to the existing ones. It should
>> be different in some way, and have a clear benefit (pointing to a paper
>> isn't enough to demonstrate a benefit, the benefit should be easy to
>> explain and demonstrated many times).
>>
>> Gaël
>>
>>
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--
Milton Pividori
Blog: www.miltonpividori.com.ar
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