Clustering by Passing Messages
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As recently published in Science (see reference)
Simple and effective means of clustering any data for which a similarity matrix can be constructed. Does not require similarity matrix meet the standards for a metric. The algorithm applies in cases where the similarity matrix is not symmetric (the distance from point i to j can be different from j to i). And it does not require triangular equalities (e.g. the hypoteneus can be less than the sum of the other sides)
usage is very simple (given an m x m similarity matrix)
ex = affprop(s)
returns ex, a m x 1 vector of indices, such that ex(i) is the exemplar for the ith point.
see affyprop_demo for a complete example with simple 2d data. See reference for more complex examples including face matching.
Cite As
Michael Boedigheimer (2024). Clustering by Passing Messages (https://www.mathworks.com/matlabcentral/fileexchange/15498-clustering-by-passing-messages), MATLAB Central File Exchange. Retrieved .
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- AI, Data Science, and Statistics > Statistics and Machine Learning Toolbox > Cluster Analysis and Anomaly Detection >
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affinity_propagation/
Version | Published | Release Notes | |
---|---|---|---|
1.0.0.0 | Improved Demo |