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It is crucial to determine the optimal number of clusters for the clustering quality in cluster analysis. From the standpoint of sample geometry, two concepts, i.e., the sample clustering dispersion degree and the sample clustering synthesis degree, are defined, and a new clustering validity index is designed. Moreover, a method for determining the optimal number of clusters based on an agglomerative...
The purpose of this paper is to propose new clustering technique on manifolds. This is achieved mainly with the help of tangent spaces that are determined by manifold learning. We embed a new searching algorithm based on differential evolution (DE). We present a simple convergence analysis with a design of experimental framework.
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