摘要
1 引言近年来,数据库的数量和单个数据库的容量都大大增长了。比如,空间物体数据库包括几十亿个望远镜图像,NASA地球观测系统每小时都会产生50GB的数据。这么大的数据量已经远远超出了人为分析解释的能力范围。数据库中的知识发现(KDD)是识别数据中有价值的、新的、潜在有用的。
Clustering is widely used in areas such as pattern recognition, data analysis, and image processing. Recently, clustering has been recognized as a primary data mining method for knowledge discovery in spatial databases. However the well-known clustering algorithms have some drawbacks when applied to large spatial databases. First, they assume that all objects are clustered in main memory. Second, these methods are too inefficient when applied to large databases. In this paper, we survey several new algorithms which make use of clustering properties of spatial index structure.
出处
《计算机科学》
CSCD
北大核心
2000年第7期43-46,共4页
Computer Science
