基于网格空间团的多级同位模式挖掘方法

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原文链接

NETL
NSTL
万方数据
维普

中文摘要：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%。

外文标题：Multi-level co-location pattern mining algorithm based on grid spatial cliques

外文摘要：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.

外文关键词：

spatial data miningmulti-level co-location patterngrid spatial cliquedensity peak clustering(DPC)

作者：

刘宇情、王丽珍、杨培忠、朴丽莎

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作者单位：

云南大学信息学院,云南昆明 650504

滇池学院理工学院,云南昆明 650228

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)

基金：

国家自然科学基金国家自然科学基金国家自然科学基金云南省基础研究计划云南省基础研究计划云南省创新团队项目云南省智能系统与计算重点实验室建设项目

项目编号：

622762276230626662266050202201AS070015202401AT0704502018HC019202205AG070003

出版年：

2024

DOI：

10.3785/j.issn.1008-973X.2024.05.005

浙江大学学报(工学版)

浙江大学

浙江大学学报(工学版)

CSTPCD北大核心

影响因子：0.625

ISSN：1008-973X

年,卷(期)：2024.58(5)

参考文献量24