Clustering analysis has been widely used in pattern recognition, image processing, machine learning and so on. It is a great challenge for most existing clustering algorithms to discover clusters with complex manifolds or great density variation. And the most of the existing clustering need manually set neighborhood parameter K to search the neighbor of each object. In this paper, we use Natural Neighbor to adaptively get the value of K and natural density of each object. Then, we define two novel concept that Natural Core Point and distance between clusters to solve the complex manifold problem. On the basis of above proposed concept, we propose a novel hybrid clustering algorithm that only need one parameter M (the number of final clusters) based on Minimum Spanning Tree of Natural Core Points, called NCP. The experimental results on synthetic dataset and real dataset show that the proposed algorithm is competitive with state-of-the-art methods when discovering with complex manifold or great density variation.
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