关键词: DBSCAN算法, 自适应, 参数寻优, K-平均最近邻法

Abstract: The traditional DBSCAN algorithm needs to manually determine Eps and MinPts parameters, and the choice of parameters directly determines the rationality of the clustering results, thus this paper puts forward a new self-adaptive parameter determination method for DBSCAN algorithm. The method, based on the parameter optimization strategy, uses the data set’s own distribution characteristics to generate candidate Eps and MinPts parameters, automatically finds the cluster number change stable interval of the clustering result, and uses the Eps and MinPts parameters corresponding to the minimum density threshold in the interval as the optimal parameters. The experimental results show that the method can fully automate the clustering process and can select reasonable Eps and MinPts parameters, and the method obtains clustering results with high accuracy.

Key words: DBSCAN algorithm, self-adaptive, parameter optimization, K-average nearest neighbor