Abstract: DBSCAN is an outstanding representative of clustering algorithms for its good performance in clustering spatial data.To improve its efficiency this paper proposes GbDBSCAN（Grid based DBSCAN）.GbDBSCAN adopts gird and data binning technique to query density reachable objects for all objects in dataset.It can also identify and handle border points.Experiment results show that GbDBSCAN is much more efficient than DBSCAN in low dimensional data space，without lowering the quality of DBSCAN.

摘要： DBSCAN是一种性能优越的基于密度的聚类算法。为提高它的运行效率，提出了基于网格的DBSCAN算法GbDBSCAN。该算法使用网格划分的方法和数据分箱技术，减少了判定密度可达对象时的搜索范围，降低了算法的运行时间，而且算法还能够识别并处理边界点。实验结果表明，GbDBSCAN在不降低DBSCAN聚类质量的前提下，大大提高了DBSCAN算法处理低维数据集的效率。