Fast and efficient spectral clustering

versión 1.10.0.0 (11.3 MB) por Ingo
Perform fast and efficient spectral clustering algorithms

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Actualizada 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.

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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 (2022). Fast and efficient spectral clustering (https://www.mathworks.com/matlabcentral/fileexchange/34412-fast-and-efficient-spectral-clustering), MATLAB Central File Exchange. Recuperado .

Compatibilidad con la versión de MATLAB
Se creó con R2011b
Compatible con cualquier versión
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ImageSegmentation/

datasets/rainbowdash/

files/

files/GUI/

files/GUI/funcs/

files/GUI/funcs/getFuncs/

files/GUI/funcs/plotFuncs/

files/GUI/funcs/setFuncs/

files/GUI/funcs/toggleFuncs/

files/SimilarityGraph/

files/other/