基于网格空间团的多级同位模式挖掘方法  

Multi-level co-location pattern mining algorithm based on grid spatial cliques

作　　者：刘宇情 王丽珍 杨培忠[1] 朴丽莎 LIU Yuqing;WANG Lizhen;YANG Peizhong;PIAO Lisha(School of Information Science and Engineering,Yunnan University,Kunming 650504,China;Institute of Science and Technology,Dianchi College of Yunnan University,Kunming 650228,China)

机构地区：[1]云南大学信息学院,云南昆明650504 [2]滇池学院理工学院,云南昆明650228

出　　处：《浙江大学学报（工学版）》2024年第5期918-930,共13页Journal of Zhejiang University：Engineering Science

基　　金：国家自然科学基金资助项目(62276227,62306266,62266050);云南省基础研究计划资助项目(202201AS070015,202401AT070450);云南省创新团队资助项目(2018HC019);云南省智能系统与计算重点实验室建设项目(202205AG070003)。

摘　　要：针对传统的多级同位模式挖掘方法未考虑到实际数据分布的网格特性,且从全局到区域的多级模式挖掘框架会导致算法效率低下的问题,提出逆向挖掘多级同位模式的新框架.先挖掘区域同位模式,再由区域同位模式推导出全局同位模式,提出有效的剪枝策略提高挖掘效率.考虑真实数据集中数据分布的网格特性,定义实例间的网格邻近关系,提出网格空间团及计算网格空间团的新颖方法.在区域划分阶段,提出基于自适应网格密度峰值聚类的区域划分方法,基于2阶网格空间团的网格相似性来分配簇.在合成和实际数据集上进行大量的实验,验证了提出方法的有效性、高效性和可扩展性,在真实数据集上的剪枝率可以达到78%.A novel framework of reverse mining of multi-level co-location patterns was proposed aiming at the problems that traditional methods of multi-level co-location pattern mining did not consider the grid characteristics of the real data distribution,and the multi-level mining framework from global to regional led to the algorithm inefficiency.The regional co-location patterns were first mined,and the global co-location patterns were deduced based on the mined regional patterns.Some pruning strategies were proposed to enhance the mining efficiency.The grid characteristics of the data distribution in real datasets were considered,and the grid neighbor relationship between instances was defined.The concept of grid spatial cliques with a novel method for calculating grid spatial cliques was defined.An adaptive grid density peak clustering strategy for partitioning regions was proposed in the regional division stage,and clusters were assigned based on the similarity of two-size grid spatial cliques.Extensive experiments were conducted on both synthetic and real-world datasets.The experimental results validated the effectiveness,efficiency and scalability of the proposed method.A pruning rate of up to 78%was achieved on real datasets.

关 键 词：空间数据挖掘 多级同位模式 网格空间团 密度峰值聚类(DPC) 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]