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.%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. Hie 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.
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