Code covered by the BSD License
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...
Handle arguments to function
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[idx,netsim,dpsim,expref,unco...
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ind2cluster(labels)
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similarity_euclid(data,vararg...
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similarity_pearson(data)
pearson coefficients between every two columns
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similarity_pearsonC(data, C)
pearson coefficients between every column and the center
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valid_errorate(labels, truela...
computing error rates for every clusters if true labels are given
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valid_external(index1,c2)
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valid_sumpearson(data,labels,...
within-, between-cluster and total sum of squares
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valid_sumsqures(data,labels,k...
data: a matrix with each column representing a variable.
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test_APclustering.m
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View all files
Semi-supervised Affinity Propagation clustering
by Kaijun Wang
07 Jan 2008
(Updated 01 Jul 2009)
embed Silhouette index into iterations of Affinity propagation clustering to supervise its running
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| File Information |
| Description |
Affinity propagation clustering (AP) is a clustering algorithm proposed in "Brendan J. Frey and Delbert
Dueck. Clustering by Passing Messages Between Data Points. Science 315, 972 (2007)". It has some advantages: speed, general applicability, and suitable for large number of clusters.
Semi-supervised AP improves AP by: embedding Silhouette indices into the programs of AP to supervise the running of AP, so that the AP will give its optimal clustering solution.
The programs of semi-supervised AP are suitable for the person who has interests in studying or improving AP algorithm, and then the semi-supervised AP may be an example for reference. |
| Required Products |
Statistics Toolbox
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| MATLAB release |
MATLAB 7.2 (R2006a)
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| Updates |
| 28 Jul 2008 |
help file is updated |
| 01 Jul 2009 |
update the license |
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