Fast and efficient spectral clustering

Perform fast and efficient spectral clustering algorithms

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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

Citar como

Ingo (2026). Fast and efficient spectral clustering (https://es.mathworks.com/matlabcentral/fileexchange/34412-fast-and-efficient-spectral-clustering), MATLAB Central File Exchange. Recuperado .

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Más información sobre Statistics and Machine Learning Toolbox en Help Center y MATLAB Answers.

Información general

Compatibilidad con la versión de MATLAB

  • Compatible con cualquier versión

Compatibilidad con las plataformas

  • Windows
  • macOS
  • Linux
Versión Publicado Notas de la versión Action
1.10.0.0

Final update including full GUI and more. See description for details.

1.8.0.0

Included acknowledgements

1.7.0.0

- Fixed critical mistake when creating similarity graphs

- Restructured some of the code

1.6.0.0

Fixed critical bug when creating sparse matrices

Demo now plots similarity graph (only use for few data points!)

Minor changes

1.5.0.0

fixed wrong code in demo file

1.4.0.0

Got rid of redundant code

1.3.0.0

Minor updates

1.1.0.0

- Updated some files
- Included Demo

1.0.0.0