Fast and efficient spectral clustering

Version (11.3 MB) by Ingo
Perform fast and efficient spectral clustering algorithms


Updated 13 Sep 2012

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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.

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).

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.

- 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

Cite As

Ingo (2023). Fast and efficient spectral clustering (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2011b
Compatible with any release
Platform Compatibility
Windows macOS Linux

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Version Published Release Notes

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

Included acknowledgements

- Fixed critical mistake when creating similarity graphs

- Restructured some of the code

Fixed critical bug when creating sparse matrices

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

Minor changes

fixed wrong code in demo file

Got rid of redundant code

Minor updates

- Updated some files
- Included Demo