Density-Based Clustering Using Variable Kernel and Maximum Entropy Principle
In this paper, we introduce a new data clustering approach. It articulates essentially on the idea that each center is characterized by a special values of density as well as distance. The variable kernel estimator is the key element in this study. Our proposed method employs the entropy to find out the optimal bandwidth, which is crucial in density estimation. Data are previously mapped through kernel function before performing principal component analysis, which is efficient method for nonlinear patterns determination. The resulting data are reduced to lower-dimension feature space where only the efficient features are selected. Experiments divulge our proposed approach efficiency.
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