直接从空间数据中挖掘频繁模式

为简化空间频繁模式挖掘的预处理步骤并提高挖掘效率, 提出一种可以直接以空间矢量和栅格图层作为输入的挖掘算法FISA(fast intersect spatial Apriori)。该算法利用图层求交和面积计算操作实现谓词集支持度计数进而实现频繁谓词集和关联规则挖掘。相对于基于事务空间关联规则挖掘算法, FISA不需要预先进行空间数据事务化处理, 并且所得结果均有对应图层, 便于实现结果的可视化; 相对于其他基于空间分析的挖掘算法, FISA支持空间数据的矢量和栅格格式, 且引入了快速求交方法以保证其可伸缩性。实验结果表明该算法可以直接从空间数据中高效正确地挖掘出频繁模式。

Mining frequent patterns directly from spatial datasets

To simplify the preprocessing procedure of spatial frequent pattern mining and enhance the efficiency, this paper proposed a mining algorithm called FISA. Using FISA frequent patterns (predicate sets and association rules) could be directly extracted from spatial datasets. Unlike transaction based mining algorithms, the complex and expert-dependent preprocessing procedure was not needed because FISA calculated the support of predicate sets using spatial intersect operation and area calculation instead of the record counting. Besides, vector or raster layers corresponding to predicate sets would be created during mining, which could be further used for visualization of mining results. Compare to other spatial analysis based mining algorithms, FISA supports both vector and raster layers, which were the majority format of spatial datasets. Also, it introduced a fast intersect method which could decrease the time complexity of support calculation into FISA to assure its scalability. Experimental results demonstrate that FISA is capable of mining frequent patterns from spatial datasets directly, correctly and efficiently.