带时间约束的co-location模式挖掘

Co-location Patterns Mining with Time Constraint

空间数据挖掘的相关研究大多数是基于理想化数据和实例平等的思想,而忽略了实际场景中存在的时间约束条件。将实例存在的时间区间作为约束条件,重新定义了空间邻近关系R,提出了带有时间约束的频繁模式挖掘算法TI,并以时间重叠作为剪枝条件,提出了剪枝算法TI-C。通过实证数据分析得出:在相同数据集下,TI-C算法的效率要优于TI,采用TI-C算法得到的频繁模式个数要比join-based算法少,同时采用TI-C算法得到的频繁模式能更精确、真实地反映实际场景中对象的并置关系。

Most of the research achievements of spatial data mining are based on the ideal spatial data and the idea of examples equality,ignoring the time constraint condition existing in the actual scene.This paper considered the existent time interval of the instance as constraint condition,redefined spatial neighborhood relation R,proposed spatial frequent pattern mining algorithm TI with time constraint,and by using time overlap as pruning condition,proposed pruning algorithm TI-C.Through empirical data analysis,under the same data set,the efficiency of TI-C algorithm is better than that of TI,the frequent pattern number of TI-C algorithm is less than that of join-based algorithm,and the frequent pattern of TI-C algorithm can accurately and truly reflect the object＇s co-location relation of the actual scene.