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.
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
Ingo (2023). Fast and efficient spectral clustering (https://www.mathworks.com/matlabcentral/fileexchange/34412-fast-and-efficient-spectral-clustering), MATLAB Central File Exchange. Retrieved .
MATLAB Release Compatibility
Platform CompatibilityWindows macOS Linux
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!Start Hunting!
Final update including full GUI and more. See description for details.
- 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!)
fixed wrong code in demo file
Got rid of redundant code
- Updated some files