【摘要】 QR-树处理海量空间数据时,其深度和R-树内目录矩形的重叠面积会变大,导致查询效率降低。针对该问题采用K-means算法对索引对象进行聚类分析,构造新的聚类中心使其能处理具有多种形体的索引对象,并在QR-树中引入超结点存储聚类结果。提出一种QCR-树空间索引结构来提高查询效率,给出QCR-树的插入、删除和查询算法。实验结果表明QCR-树的查询性能优于QR-树,适用于海量数据。更多还原

【Abstract】 The depth of QR-tree and the overlapping areas of directory rectangles of R-tree will increase when the massive spatial data is processed by the QR-tree,which incures lower query efficiency.Aiming at this problem,this paper carries out clustering analysis of index objects by K-means algorithm,and a novel formula of clustering center is constructed to make K-means deal with index objects with various forms.It introduces super nodes for storing the clustering results and proposes a QCR-tree spatial index structure to improve the query efficiency.The insertion,deletion and query algorithms of QCR-tree are presented.Experimental results show that QCR-tree,whose query performance is higher than QR-tree,is fit for processing the massive data.更多还原