异质环境下的空间关联规则挖掘

Algorithm of Mining Spatial Association Data Under Spatially Heterogeneous Environment

分析了异质空间情形下的空间关联规则挖掘特征,给出了异质环境下空间关联规则挖掘的几个重要指标及计算方法。在实际中应用该方法,能有效地取得空间关联规则及由异质性导致的表现区域的差异,真实地反映事物的客观规律。

Spatial heterogeneity widely exists in the nature, Traditional spatial association data mining assumes that the area on which the mining algorithm performs is evenly distributed, which leads to the mismatch between the mined knowledge and the reality. We suggest that spatial association mining should consider this spatial heterogeneity when designing mining algorithms. The characteristics of spatial association mining were analyzed. Three key measuring indexes indicating spatial association strength were defined. The method of calculating the indexes was presented. The algorithm for mining spatially heterogeneous association patterns and their corresponding subregions in which the pattern shows strong association was proposed. Practical application proved that the proposed strategy was valuable and effective in mining spatial association patterns under spatially heterogeneous environment.