Fast and efficient spectral clustering
Perform fast and efficient spectral clustering algorithms
SpectralClustering performs one of three spectral clustering algorithms (Unnormalized, Shi & Malik, Jordan & Weiss) on a given adjacency matrix. SimGraph creates such a matrix out of a given set of data and a given distance function.
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UPDATE 09/13/2012
This major update to the final version includes
[+] Full GUI
[+] Several Plot Options: 2D/3D, Star Coordinates, Matrix Plot
[+] Save Plots
[+] Save and Load all kind of data (pure data, similarity graph, clustered data)
[+] Differentiates between already labeled and unlabeled data (see README).
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The code has been optimized (within Matlab) to be both fast and memory efficient. Please look into the files and the Readme.txt for further information.
References:
- Ulrike von Luxburg, "A Tutorial on Spectral Clustering", Statistics and Computing 17 (4), 2007
If there are any questions or suggestions, I will gladly help out. Just contact me at admin (at) airblader (dot) de
引用格式
Ingo (2026). Fast and efficient spectral clustering (https://ww2.mathworks.cn/matlabcentral/fileexchange/34412-fast-and-efficient-spectral-clustering), MATLAB Central File Exchange. 检索时间: .
| 版本 | 已发布 | 发行说明 | Action |
|---|---|---|---|
| 1.10.0.0 | Final update including full GUI and more. See description for details. |
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| 1.8.0.0 | Included acknowledgements |
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| 1.7.0.0 | - Fixed critical mistake when creating similarity graphs - Restructured some of the code |
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| 1.6.0.0 | Fixed critical bug when creating sparse matrices Demo now plots similarity graph (only use for few data points!) Minor changes |
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| 1.5.0.0 | fixed wrong code in demo file |
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| 1.4.0.0 | Got rid of redundant code |
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| 1.3.0.0 | Minor updates |
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| 1.1.0.0 | - Updated some files
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| 1.0.0.0 |
