【摘要】 在空间关联规则挖掘中一般是采用遍历算法进行,导致对海量数据计算效率的降低。目前,空间数据挖掘模型多采用空间邻接矩阵来表达空间关联权重,大多情况下没有考虑邻接关系的实际量化的结果。文中在分析了空间实体分布的各种相邻关系基础上,采用邻接指数的方式来测算空间相关程度,并在此基础上采用改进的Apriori算法,通过自编程序加以实现。以北京市昌平区土地利用类型的空间分布关系为样例数据进行了试算。结果表明,计算效率有较大提高,并挖掘出一些潜在的土地利用类型间的共生关系。更多还原

【Abstract】 Traverse algorithm is used in spatial association rules mining,which result in the lower efficiency in massive data mining.Currently,spatial data mining model expresses spatial association weight with spatial adjacent matrix without consideration of quantitative adjacent results.By analyzing spatial entities’ distribution and various neighbor relationships,this paper used adjacent index to calculate spatial association degree,and improved Apriori algorithm.Taking Changping district of Beijing as the sample data,the results showed that algorithm efficiency has improved greatly,and a number of potential symbiotic relationships between the land-use types has been mined.更多还原