Abstract: Under the bigger data conditions, the traditional spatial relationships describe only two space objects and show many problems of redundant data storage, retrieval and so on. Firstly, the method uses clustering algorithm for clustering space objects, and then indicates the direction of relationship between space objects which are based on the clustering results. Thus this paper proposes clustering algorithm based on K-means and density, and indicates the method of combination of the direction relationship and clustering algorithm. The methods enhance the ability of handling massive spatial data in spatial database, save a lot of storage space and improve the query speed of data.

Key words: clustering algorithm, K-means, spatial relationship, direction relationship

摘要： 在大数据量的环境下，传统空间数据的空间关系仅描述两个空间物体，从而出现数据存储冗余，检索速度慢等问题。提出改进的聚类算法对空间物体聚类，再在聚类结果的基础上表示空间物体的方向关系。提出了基于密度的K-均值算法和空间聚类与方向关系融合的新方法。所提方法增强了空间数据库对空间数据对象的空间方向关系的智能处理能力，节省了存储空间，提高了数据的查询速度。

关键词: 空间聚类, K均值, 空间关系, 方向关系