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Directly generating a user specified K clusters with message passing (Affinity Propagation) 1.0

  Date Added: August 13, 2013  |  Visits: 174

Directly generating a user specified K clusters with message passing (Affinity Propagation)

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The Affinity Propagation (AP) clustering algorithmproposed by Frey and Dueck (2007) provides an understandable,nearly optimal summary of a data set. However,it suffers two major shortcomings: i) the number of clusters isvague with the user-defined parameter called self-confidence,and ii) the quadratic computational complexity. When aimingat a given number of clusters due to prior knowledge, APhas to be launched many times until an appropriate settingof self-confidence is found. The re-launched AP increases thecomputational cost by one order of magnitude. We propose an algorithm, called K-AP, to exploit the immediateresults of K clusters by introducing a constraint in theprocess of message passing. Through theoretical analysis andexperimental validation, K-AP was shown to be able to directlygenerate K clusters as user defined, with a negligible increaseof computational cost compared to AP. In the meanwhile, KAPpreserves the clustering quality as AP in terms of thedistortion. K-AP is more effective than k-medoids w.r.t. thedistortion minimization and higher clustering purity (algorithm is described in ICDM'2010).http://www.lri.fr/~xlzhang .

Requirements: No special requirements
Platforms: Matlab
Keyword: Analysis Constraint Experimental Message Passing Process Shown Theoretical Validation
Users rating: 0/10

License: Shareware Size: 20.48 KB
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DIRECTLY GENERATING A USER SPECIFIED K CLUSTERS WITH MESSAGE PASSING (AFFINITY PROPAGATION) RELATED
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