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

[樊 书华]

Hi,

Thanks for your suggestion of the paper. However, the paper shows many more algorithms and finds out different algorithms show different performance on dataset with various dimensions, Lloyd algorithm not included. What I want to know is that can we remove the Lloyd algorithm in kmeans of scikit-learn since elkan is an optimized on with better performance.

Best regards,
George

From: scikit-learn <scikit-learn-bounces+mc_george123=hotmail.com at python.org> On Behalf Of Andreas Mueller
Sent: Saturday, March 28, 2020 12:37 AM
To: scikit-learn at python.org
Subject: Re: [scikit-learn] A basic question about kmeans algorithms elkan and llyod

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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