基于DS理论的不确定空间co-location模式挖掘

 提出一种基于DS理论的co-location挖掘方法.先将数据集按照空间对象分类，然后从分类后的子数据集中依次提取该空间对象的实例，与其他空间对象数据集中的各个实例进行配对，将找到的各实例关系记录到一个新的关系表中，作为使用DS理论求解的基础.从一个新的角度来研究co-location挖掘技术，在DS理论的基础上重新定义了参与度，基于新定义的参与度来求解co-location模式.

Mining co-location patterns over uncertain data based on DS evidence theory

This paper proposed a method of co-location pattern mining over uncertain data based on DS evidence theory.Firstly,the spatial objects were classified into data subsets.Then,selecting the instances of a spatial object from the classified subsets successively matched each instance in the rest of spatial object subsets.Finally,for using DS theory to solve co-location pattern mining,the found instance relations were recorded into a new table.In this paper,a new perspective to study the co-location mining.Based on the DS theory,a new concept of the participation index was defined,and a co-location pattern mining algorithm was designed based on the new definition.