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<a href="Scientific-module.html">Package Scientific</a> ::
<a href="Scientific.Clustering-module.html">Package Clustering</a> ::
Module AffinityPropagation
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<h1 class="epydoc">Module AffinityPropagation</h1><p class="nomargin-top"></p>
<p>Clustering by Affinity Propagation</p>
<p>This clustering algorithm identifies clusters in a set of data items
based on a list of similarities between the items. The result is a list
of clusters, each cluster being defined by one 'exemplar' (the item that
is most representative of the cluster) and by other items. The number of
clusters is not specified in advance. Instead, a parameter called
'preference' indicates how likely each item is to be an exemplar. Often
it is set to the same value for all items. Low preference values yield
few big clusters, whereas high preference values yield many small
clusters.</p>
<p>The algorithm is described in: B.J. Frey & D. Dueck, Science 315,
972-976 (2007)</p>
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A collection of data items with similarities
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