关键词: 动态近邻, DBSCAN算法, [K]近邻, 近邻密度, 相对密度变化率

Abstract: DBSCAN is a classic density-based clustering algorithm. DBSCAN algorithm has many flaws, for example: difficult to obtain sensitive parameter, can’t adapt to uneven density data because of the fixed parameter. The concept of dynamic neighbors is proposed, namely parameters changed with dynamic density. The neighbor scale evolution algorithm is designed to adjust the dynamic parameters, namely by limiting the relative density gradient, adjust the neighbor size step by step. Finally, according to the size of dynamic neighbors, it redefines the concept of core object, and designs the DN-DBSCAN algorithm. The simulation result shows that DN-DBSCAN can effectively identify the no convex and uneven distribution of density data samples, the clustering effect is better than the traditional DBSCAN algorithm and other classical improved algorithms.

Key words: dynamic neighbor, DBSCAN algorithm, [k]-nearest neighbor, neighbor density, relative density gradient