计算机工程与应用2019，Vol.55Issue(5)：1-7,148,8.DOI:10.3778/j.issn.1002-8331.1809-0018
自适应确定DBSCAN算法参数的算法研究
Research on Method of Self-Adaptive Determination of DBSCAN Algorithm Parameters
李文杰 1闫世强 1蒋莹 1张松芝 1王成良1
作者信息
摘要
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.关键词
DBSCAN算法/自适应/参数寻优/K-平均最近邻法Key words
DBSCAN algorithm/ self-adaptive/ parameter optimization/ K-average nearest neighbor分类
信息技术与安全科学引用本文复制引用
李文杰,闫世强,蒋莹,张松芝,王成良..自适应确定DBSCAN算法参数的算法研究[J].计算机工程与应用,2019,55(5):1-7,148,8.
