Sparsified K-Means

Version 1.0.0.0 (825 ko) par Stephen Becker

Extremely fast K-Means for big data

https://github.com/stephenbeckr/SparsifiedKMeans

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Mise à jour 18 avr. 2018

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KMeans for big data using preconditioning and sparsification, Matlab implementation. This has three main features:
(1) it has good code: same accuracy and 100x faster than Matlab's K-means for some cases. It also incorporates the latest research, such as using K-Means++ for the initialization (Note: Matlab's R2015 K-Means now uses K-Means++ too). The code is well-documented and conforms to the conventions of Matlab's K-means function when possible.
(2) optionally, you can enable the precondition-and-sample feature which is a novel method to allow efficient processing when the datasets are extremely large and slow to work with.

(3) for datasets that are a few TB in size, you can use the read-from-disk option so that the entire matrix is never loaded into RAM all at once.

Installation is easy; run `setup_kmeans.m` and it will install the mex files for you if necessary, and setup the appropriate paths.

Citation pour cette source

Stephen Becker (2024). Sparsified K-Means (https://github.com/stephenbeckr/SparsifiedKMeans), GitHub. Récupéré le juillet 23, 2024.

Compatibilité avec les versions de MATLAB

Créé avec R2013a

Compatible avec toutes les versions

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Windows macOS Linux

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Inspiré par : KMeans Segmentation - MEX, k-means++, Fast K-means, Fast K-means clustering, Fast kmeans Algorithm Code, K-means clustering

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Les versions qui utilisent la branche GitHub par défaut ne peuvent pas être téléchargées

Version	Publié le	Notes de version
1.0.0.0	18 avr. 2018	Fixed typos in the description, no change to code (but github version is updated regularly)

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