关键词: 聚, 类，距离突变，子簇合并，核密度估计

Abstract:

Clustering algorithms are commonly difficult to determine parameters and to adapt to datasets of various shapes. And their time complexity increses with theire improvement of universality. To solve these problems,we propose a new clustering algorithm which finds the mutation position of the sample point distance by combining the global and the local features，combines the global and the local features look for the mutation position of the sample point distance，and realizes the clustering of the convex spherical datasets by calculating the minimum distance within the cluster of the sample point. On this basis,we propose the concept of sub?cluster connectivity which adapts to datasets of various shapes by merging sub?clusters based on two easily determinded parameters. Comparing this algorithm with DBSCAN （Density?Based Spatial Clustering of Applications with Noise） and other clustering algorithms on four classic datasets，the experimental results show that the proposed algorithm can be applied to datasets with complex cluster shapes，and has faster calculation speed than algorithms with the same clustering ability. Its advantages include fewer parameters which are easier determined with excellent comprehensive performance.

Key words: clustering, distance mutation, sub?cluster merge, kernel density estimation clustering