基于K-Means聚类的R-树空间索引方法研究与分析

 空间聚类和空间索引的结合是当前空间数据库中提高数据检索效率的技术之一。本文从空间聚类和空间索引的存储原理入手,阐述了K-Means聚类算法及其改进算法的技术思路,研究了K-Means算法在空间数据库中与空间索引方法结合的技术问题;分析了当前基于K-Means算法的R-树系列空间索引技术的研究成果,阐述了它们提高空间检索效率的技术路线及实验结果,研究显示这些技术都能在一定程度上提高数据检索的效率。最后给出了聚类与空间索引结合技术未来的研究方向。

R-tree Spatial Index Based on K-Means Clustering

At present, combining spatial clustering with spatial index is one of the techniques that could enhance data retrieval efficiency in the spatial database research. Based on the storage principles of spatial clustering and spatial index, K-Means clustering algorithm and the technical ideas behind improved algorithm are elaborated; the techniques of combining K-Means algorithm with spatial index method in spatial database are studied. Current research results of R-tree series spatial index technology based on K-Means algorithm air analyzed and their technical ideas for improving spatial retrieval efficiency and experiment results are described. The research shows that these techniques could enhance data retrieval efficiency to a certain degree. In the end, the future research trend of technique about combining clustering with spatial index is proposed.