[scikit-learn] A basic question about kmeans algorithms elkan and llyod

[Andreas Mueller]

There's an interesting analysis in this paper:
Fast K-Means with Accurate Bounds

http://proceedings.mlr.press/v48/newling16.pdf


On 3/26/20 3:40 AM, Alexandre Gramfort wrote:
> hi,
>
> I suspect Elkan is really winning when you have many centroids
> so the conclusion is not systematic
>
> my 2c
> Alex
>
>
> On Thu, Mar 26, 2020 at 3:18 AM MC_George123 at hotmail.com 
> <mailto:MC_George123 at hotmail.com> <MC_George123 at hotmail.com 
> <mailto:MC_George123 at hotmail.com>> wrote:
>
>     Hi admins,
>
>     My team is working on optimization on scikit-learn staff now. When
>     it comes to kmeans, I find there are two algorithms, one of which
>     is lloyd and the other is elkan, which is the optimized one for
>     lloyd using triangle inequality.  In the older version of
>     scikit-learn, elkan only supports dense dataset instead of sparse
>     one. And in the latest version, elkan supports both type of
>     datasets. So there is a question why both two algorithms are kept
>     in kmeans since they do the almost same thing and elkan is a
>     optimized one for lloyd. Are there any precision difference
>     between two algorithms and how can I decide what algorithm to use?
>
>     Best regards,
>
>     George Fan
>
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