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Fast and efficient spectral clustering

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Fast and efficient spectral clustering

by

Ingo

 

02 Jan 2012 (Updated )

Perform fast and efficient spectral clustering algorithms

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Description

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

Acknowledgements

Relativepath.M and Export Fig inspired this file.

Required Products Statistics Toolbox
MATLAB
MATLAB release MATLAB 7.13 (R2011b)
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Comments and Ratings (12)
24 Mar 2014 Toby Driscoll

Toby Driscoll

 
19 Jul 2013 Ingo

Ingo

@Hanan That sure will be a test and I assume you need a good computer, but I think it should be possible. You would just have to test it, though.

Comment only
18 Jul 2013 Hanan Shteingart

Hanan Shteingart

does this package supports "large" datasets of 100,000 rows on 500 columns?

Comment only
02 Jul 2013 Javier

Javier

This program fails on Matlab's Linux version since the relativepadth() function returns the relative path in lower letters.

The solution is to use an improved version of relativepath such as this one:
http://www.mathworks.com/matlabcentral/fileexchange/41253-improved-relativepath-m

Comment only
18 Apr 2013 Charles Nelatury

Charles Nelatury

Thanks!

02 Nov 2012 Ingo

Ingo

@BD Knight: Take a look at the SpectralClustering.m and the SimGraph_xxx.m files. Those are really all you need and they are well documented.

Comment only
02 Nov 2012 Eric T

Eric T

Instructions on using command line? I need clustering embedded deeply in other tasks, multiple times, so using a GUI is not an option.

Comment only
04 Jul 2012 Ingo

Ingo

@leile: The code supports data with any dimension. By the way, a major update will be released as soon as I handed my thesis in. This will include a fully functional GUI.

Comment only
03 Jul 2012 leila

leila

Does the code support 3d data?

Comment only
02 Jul 2012 Xiao

Xiao

 
08 Feb 2012 Yo Yo

Yo Yo

 
03 Feb 2012 Jeff Harris

Jeff Harris

Nicely done; simple and efficient code.

Updates
03 Jan 2012

- Updated some files
- Included Demo

04 Jan 2012

Minor updates

08 Jan 2012

Got rid of redundant code

08 Jan 2012

fixed wrong code in demo file

13 Jan 2012

Fixed critical bug when creating sparse matrices

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

Minor changes

19 Jan 2012

- Fixed critical mistake when creating similarity graphs

- Restructured some of the code

24 Apr 2012

Included acknowledgements

13 Sep 2012

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

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