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Directly generating a user specified K clusters with message passing (Affinity Propagation) 1.0
File ID: 79815






Directly generating a user specified K clusters with message passing (Affinity Propagation) 1.0
Download Directly generating a user specified K clusters with message passing (Affinity Propagation) 1.0http://www.mathworks.comReport Error Link
License: Shareware
File Size: 20.5 KB
Downloads: 15
Submit Rating:
Directly generating a user specified K clusters with message passing (Affinity Propagation) 1.0 Description
Description: The Affinity Propagation (AP) clustering algorithm
proposed 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 is
vague with the user-defined parameter called self-confidence,
and ii) the quadratic computational complexity. When aiming
at a given number of clusters due to prior knowledge, AP
has to be launched many times until an appropriate setting
of self-confidence is found. The re-launched AP increases the
computational cost by one order of magnitude. We propose an algorithm, called K-AP, to exploit the immediate
results of K clusters by introducing a constraint in the
process of message passing. Through theoretical analysis and
experimental validation, K-AP was shown to be able to directly
generate K clusters as user defined, with a negligible increase
of computational cost compared to AP. In the meanwhile, KAP
preserves the clustering quality as AP in terms of the
distortion. K-AP is more effective than k-medoids w.r.t. the
distortion minimization and higher clustering purity (algorithm is described in ICDM'2010).
http://www.lri.fr/~xlzhang .

License: Shareware

Related: passing, Message, theoretical, Analysis, shown, Validation, experimental, Process, Constraint

O/S:BSD, Linux, Solaris, Mac OS X

File Size: 20.5 KB

Downloads: 15



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