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Clustering by Passing Messages Between Data Points

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Clustering data by identifying a subset of representative examples is important for processing sensory signals and detecting patterns in data. Such “exemplars” can be found by randomly choosing an initial subset of data points and then iteratively refining it, but this only works well if that initial choice is close to a good solution. We describe a new method called “affinity propagation,” which takes as input measures of similarity between pairs of data points. Real-valued messages are exchanged between data points until a high-quality set of exemplars and corresponding clusters gradually emerges.

Appeared in Science February 2007 http://www.sciencemag.org/cgi/content/abstract/1136800v1 by Brendan Frey and Delbert Dueck

This talk is part of the Machine Learning Journal Club series.

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