Dynamic pattern denoising method using multi-basin system with kernels
Pattern Recognition
Forecasting trends of high-frequency KOSPI200 index data using learning classifiers
Expert Systems with Applications: An International Journal
Uniformly subsampled ensemble (USE) for churn management: Theory and implementation
Expert Systems with Applications: An International Journal
Transductive Bayesian regression via manifold learning of prior data structure
Expert Systems with Applications: An International Journal
Forecasting nonnegative option price distributions using Bayesian kernel methods
Expert Systems with Applications: An International Journal
Sequential manifold learning for efficient churn prediction
Expert Systems with Applications: An International Journal
Position regularized Support Vector Domain Description
Pattern Recognition
Probabilistic generative ranking method based on multi-support vector domain description
Information Sciences: an International Journal
Inductive manifold learning using structured support vector machine
Pattern Recognition
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Clustering methods utilizing support estimates of a data distribution have recently attracted much attention because of their ability to generate cluster boundaries of arbitrary shape and to deal with outliers efficiently. In this paper, we propose a novel dissimilarity measure based on a dynamical system associated with support estimating functions. Theoretical foundations of the proposed measure are developed and applied to construct a clustering method that can effectively partition the whole data space. Simulation results demonstrate that clustering based on the proposed dissimilarity measure is robust to the choice of kernel parameters and able to control the number of clusters efficiently.