摘要
空间关联规则是空间数据挖掘(SDM)中的重要内容之一。由于空间数据的复杂性,传统的空间关联规则挖掘方法主要是将空间数据库变换为非空间数据库,通过挖掘算法挖掘空间关联规则。目前,Apriori算法是关联规则挖掘中使用最为普遍的算法,但是,由于该算法在关联规则提取过程中需要多次扫描数据库,并且产生冗余的候选项集,因此,在执行大型数据库的关联规则挖掘时,具有效率低下的缺陷。本文基于Apriori算法提出了基于布尔矩阵的空间关联规则挖掘算法,并以挖掘福建省厦门市土地覆盖现状与地形特征因子的空间关联关系作为试验案例,对比Apriori算法的提取结果与提取效率,结果表明:该算法不仅减少了扫描数据库的次数,而且减少了冗余候选项集的产生,提高了空间关联规则的提取效率。
Mining spatial association rules is one of the most important branches in the field of Spatial Data Mining（ SDM）. Because of the complexity of spatial data,a traditional method in extracting spatial association rules is to transform spatial database into general transaction database. The Apriori algorithm is one of the most commonly used methods in mining association rules at present. But a shortcoming of the algorithm is that its performance on the large database is inefficient. The present paper proposed a new algorithm by extracting maximum frequent itemsets based on a Boolean matrix. And a case study about extracting the spatial association rules between land cover and terrain factors was demonstrated to show the validation of the new algorithm. Finally,the conclusion was reached by the comparison between the Apriori algorithm and the new one which revealed that the new algorithm improves the efficiency of extracting spatial association rules.
出处
《测绘与空间地理信息》
2014年第5期123-126,130,共5页
Geomatics & Spatial Information Technology
