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....@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_george...@hotmail.com<mailto:mc_george...@hotmail.com> <mc_george...@hotmail.com<mailto:mc_george...@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 _______________________________________________ scikit-learn mailing list scikit-learn@python.org<mailto:scikit-learn@python.org> https://mail.python.org/mailman/listinfo/scikit-learn _______________________________________________ scikit-learn mailing list scikit-learn@python.org<mailto:scikit-learn@python.org> https://mail.python.org/mailman/listinfo/scikit-learn
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