Spectral Clustering Algorithm of Uncertain Objects

Tong He

doi:10.4028/www.scientific.net/AMM.300-301.1058

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 300-301Spectral Clustering Algorithm of Uncertain Objects

Spectral Clustering Algorithm of Uncertain Objects

Abstract:

By extending classical spectral clustering algorithm, a new clustering algorithm of uncertain objects is proposed in this paper. In the algorithm, each uncertain object is represented as a Gaussian mixture model, and Kullback-Leibler divergence and Bayesian probability are respectively used as similarity measure between Gaussian mixture models. In an extensive experimental evaluation, we not only show the effectiveness and efficiency of the new algorithm and compare it with CLARANS algorithm of uncertain objects.

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Periodical:

Applied Mechanics and Materials (Volumes 300-301)

Pages:

1058-1061

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.300-301.1058

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Online since:

February 2013

Authors:

Tong He

Keywords:

Bayesian Probability, Gaussian Mixture Model, Kullback–Leibler Divergence, Spectral Clustering Algorithm, Uncertain Objects

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