基于限定区域数据取样的密度聚类算法

传统密度算法DBSCAN与DBRS的缺点在于时间性能和聚类精度均较低，为此，提出一种结合限定区域数据取样技术的密度聚类算法——DBLRS。该算法在不增加时间和空间复杂度的基础上利用参数Eps查找核心点的邻域点和扩展点，并在限定区域(Eps,2Eps)内进行数据抽样。实验结果表明，限定区域内选取代表点进行簇的扩充降低了大簇分裂的概率，提高了算法效率与聚类精度。

Density-based clustering algorithm combined with limited regional sampling

Concerning the inefficient time performance and lower clustering accuracy revealed by the traditional density-based algorithms of DBSCAN and DBRS,this paper proposed an improved density-based clustering algorithm called DBLRS,which is combined with limited regional sampling technique.The algorithm used the parameter Eps to search for the neighborhood and expanded points of a core point without increasing time and space complexity,and implemented data sampling in a limited area(Eps,2Eps).The experimental results confirm that DBLRS can reduce the probability of large clusters’ splitting and improve the algorithmic efficiency and clustering accuracy by selecting representative points to expand a cluster.