Thank you very much for your information.
From: scikit-learn <scikit-learn-bounces+mc_george123=hotmail.com@python.org>
On Behalf Of Andreas Mueller
Sent: Tuesday, March 31, 2020 3:04 AM
To: scikit-learn@python.org
Subject: Re: [scikit-learn] A basic question about kmeans algorithms elkan and llyod
sorry I thought it also did experiements on what they call "sta" but I guess they are not included.
The conclusion is the same, though. Different algorithms show different performance on different datasets.
The Yingyang k-means has some elkan vs lloyd figures:
http://proceedings.mlr.press/v37/ding15.pdf
In table 2, the Elkan row, in cases the speedup is <1, it means elkans is slower than lloyd.
Elkans is also more memory intensive, so you can see some missing values in that where the computation couldn't be performed, but lloyd could.
On 3/30/20 3:33 AM,
樊 书华 wrote:
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@python.org> On Behalf Of Andreas Mueller
Sent: Saturday, March 28, 2020 12:37 AM
To: scikit-learn@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@hotmail.com <MC_George123@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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