：DBSCAN是一个基于密度的聚类算法。该算法将具有足够高密度的区域划分为簇，并可以在带有“噪声”的空间数

据库中发现任意形状的聚类。但DtLqCAN算法没有考虑非空间属性，且DBSCAN算法需扫描空间数据库中每个点的e一

邻域来寻找聚类，这使得DBSCAN算法的应用受到了一定的局限。文中提出了一种基于DBSCAN的算法，可以处理非空

间属性，同时又可以加快聚类的速度。-: DBSCAN is a density-based clustering algorithm. The algorithm has a sufficiently high density area is divided into clusters, and to be with the " noise"　found in the spatial database clusters of arbitrary shape. But DtLqCAN algorithm did not consider non-spatial attributes, and spatial database DBSCAN algorithm to be scanned for each point e in the neighborhood to find a cluster, DBSCAN algorithm which makes the application subject to certain limitations. In this paper, an algorithm based on DBSCAN can handle the non-spatial attributes, can also speed up the clustering speed.