关键词: DBSCAN, 网格索引, 空间数据, 聚类

Abstract: In order to improve the efficiency of clustering algorithm based on density and avoid redundant search in processing，the paper puts forward an improved spatial data clustering algorithm based on DBSCAN.The algorithm uses the method of object’s neighborhood spatial segmentation，and makes use of index of gridding structure.In core points’ neighborhood，the objects without mark which lie in eight aspects and have the biggest distance from core objects are chose to expand seed objects.In the case，the times of query is decreased，and the time complexity of clustering is reduced.In experiment，mass data is used to test the algorithm，which proves that the new algorithm’s time efficiency is much better than DBSCAN in the same clustering precision.

Key words: Density Based Spatial Clustering of Application with Noise（DBSCAN）, index of gridding, spatial data, clustering