A Novel Hybrid Clustering Algorithm Based on Minimum Spanning Tree of Natural Core Points

Abstract

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. Most of the existing clustering needs manually set neighborhood parameter <inline-formula> <tex-math notation= LaTeX >${K}$ </tex-math></inline-formula> to search the neighbor of each object. In this paper, we use natural neighbor to adaptively get the value of <inline-formula> <tex-math notation= LaTeX >${K}$ </tex-math></inline-formula> and natural density of each object. Then, we define two novel concepts, natural core point and the 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 needs one parameter <inline-formula> <tex-math notation= LaTeX >${M}$ </tex-math></inline-formula> (the number of final clusters) based on minimum spanning tree of natural core points, called NCP. The experimental results on the synthetic dataset and real dataset show that the proposed algorithm is competitive with the state-of-the-art methods when discovering with the complex manifold or great density variation.