Spectral Clustering Algorithm of Uncertain Objects
Tong He · Applied Mechanics and Materials · 2013
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.