不均匀模糊空间对象的分层次co-location模式挖掘方法

针对现有的co-location模式挖掘算法无法有效处理不均匀分布空间对象的问题，提出一种不均匀模糊空间对象的分层次co-location模式挖掘方法。首先提出一种不均匀数据集的生成方法；然后对不均匀分布的数据集进行层次划分，使每个区域具有均匀的空间分布；再基于改进的PO_RI_PC算法对划分后的模糊对象进行空间数据挖掘。该方法基于距离变化系数构建每个子区域的邻域关系图，进而完成区域融合，实现co-location模式挖掘。实验结果表明，与传统方法相比，所提方法的执行效率更高，随实例个数和不均匀度的变化获得的co-location集个数更多，同比情况下平均提高约25%，获得了更精确的挖掘结果。

Hierarchical co-location pattern mining approach of unevenly distributed fuzzy spatial objects

Focusing on the issue that the existing co-location pattern mining algorithms fail to effectively address the problem of unevenly distributed spatial objects, a hierarchical co-location pattern mining approach of unevenly distributed fuzzy spatial objects was proposed. Firstly, an unevenly distributed dataset generation method was put forward. Secondly, the unevenly distributed dataset was partitioned by a hierarchical mining method in order to provide each region with an even spatial distribution. Finally, the spatial data mining of the separated fuzzy objects was conducted by means of the improved PO_RI_PC algorithm. Based on the distance variation coefficient, the neighborhood relationship graph for each sub-region was constructed to complete the regional fusion, and then the co-location pattern mining was realized. The experimental results show that, compared to the traditional method, the proposed method has higher execution efficiency. With the change of the number of instances and uneven degree, more co-location sets are mined, and the average increase reaches about 25% under the same condition, more accurate mining results are obtained through this method.