Abstract：The current clustering methods are difficult to handle the complicated problems in which shapes and densities are changing along with the data. To overcome the shortcomings of existing clustering methods, based on discrete topological manifold created in the data space, the structural similarity of samples and the class structure are described by accessibility after defining two new relativity metrics: the neighborhood density similarity and the smoothness of neighborhood density changes. The class structure relationship is proved to an equivalence relation. Then, a clustering algorithm is designed based on compressive transformation by treating the structural similarity defined on samples as the attractiveness. The algorithm is designed to handle data with any shapes and any density, maintaining good interpretability and many other advantages. Experimental result on the artificial data sets and standard data sets shows that the method is superior to the state-of-the-art methods.
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2011, Vol. 24 
