This is a super duper fast implementation of the kmeans clustering algorithm. The code is fully vectorized and extremely succinct. It is much much faster than the Matlab builtin kmeans function. The kmeans++ seeding algorithm is also included (kseeds.m) for good initialization. Therefore, this package is not only for coolness, it is indeed practical.
Please try the demo script in the package.
Detail explanation of this algorithm can be found in following blog post:
http://statinfer.wordpress.com/2011/12/12/efficient-matlab-ii-kmeans-clustering-algorithm/
This function is now a part of the PRML toolbox (http://www.mathworks.com/matlabcentral/fileexchange/55826-pattern-recognition-and-machine-learning-toolbox).
Citar como
Mo Chen (2024). Kmeans Clustering (https://www.mathworks.com/matlabcentral/fileexchange/24616-kmeans-clustering), MATLAB Central File Exchange. Recuperado .
Compatibilidad con la versión de MATLAB
Compatibilidad con las plataformas
Windows macOS LinuxCategorías
- AI and Statistics > Statistics and Machine Learning Toolbox >
- AI and Statistics > Statistics and Machine Learning Toolbox > Cluster Analysis and Anomaly Detection >
Etiquetas
Agradecimientos
Inspirado por: Pattern Recognition and Machine Learning Toolbox
Inspiración para: Wavelet Based Image Segmentation, k-means++, Kmeans, Kernel Learning Toolbox, Logistic Regression for Classification, Naive Bayes Classifier, Kernel Kmeans
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kmeans/
Versión | Publicado | Notas de la versión | |
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2.0.0.0 | tweak and require Matlab R2016b or later
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1.9.0.0 | tuning
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1.7.0.0 | Cleaning up |
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1.5.0.0 | remove empty clusters according to suggestion |
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1.4.0.0 | remove empty clusters according to suggestion |
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1.3.0.0 | fix a bug for 1d data |
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1.2.0.0 | update the files and description |
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1.0.0.0 |