Adaptive Density Clustering Though Supervised Rough Neighborhood System
This paper generates the density based expanding sequence of sphere-distributing objects on the basis of OPTICS viewable density model,and obtains clustering number and clustering neighborhoods by exponentially reducing the self-adaptability threshold.It also acquires the lower and upper approximation regions of all clustering kernels based on rough set theory,and achieves the explicit object-dividing method by means of their neighborhood system.It determines clustering number and clustering core according to the clustering neighborhood of the objects and divides the objects in terms of rough set approximation,so that clustering has the traits of density with self-adaptability and acnodes with non-sensitivity,and sampling accelerates the algorithms effectively.