MLCPM-UC:一种基于模式实例分布均匀系数的多级co-location模式挖掘算法  

MLCPM-UC:A Multi-level Co-location Pattern Mining Algorithm Based on Uniform Coefficient of Pattern Instance Distribution

作　　者：刘新斌 王丽珍[1] 周丽华[1] LIU Xin-bin;WANG Li-zhen;ZHOU Li-hua(School of Information Science and Engineering,Yunnan University,Kunming 650500,China)

机构地区：[1]云南大学信息学院,昆明650500

出　　处：《计算机科学》2021年第11期208-218,共11页Computer Science

基　　金：国家自然科学基金项目(61966036,61662086,61762090);云南省创新团队基金项目(2018HC019);云南大学研究生科研创新基金项目(2020315)。

摘　　要：空间co-location(并置)模式是一组空间特征的子集,其实例在空间中频繁地邻近出现。由于空间数据同时存在关联性和异质性,co-location模式实例的分布或在整个研究区域中全局出现(全局co-location模式),或在研究区域的局部区域出现(区域co-location模式),从而提出了多级co-location模式挖掘。当前的多级co-location模式挖掘方法存在两个问题:1)已有的多级co-location模式挖掘方法忽略了模式在空间中的分布特性,未能准确区分全局和区域co-location模式;2)已有的多级模式挖掘方法将全局非频繁co-location模式作为候选区域co-location模式,导致候选区域co-location模式数量过多。针对以上问题,首先,定义了模式的实例分布均匀系数,在考虑模式频繁性的同时考虑了模式在空间中的分布情况,从而正确、高效地识别出全局和区域co-location模式。其次,基于模式的实例分布均匀系数,设计了一个有效的多级co-location模式挖掘算法,提出了有效的剪枝策略以提高算法效率。最后,在真实和合成数据集上进行了广泛的实验,验证了所提方法的正确性和高效性。The spatial co-location pattern is a set of spatial features,and the instances frequently appear together in the spatial region.Due to the correlation and heterogeneity of spatial data,the distribution of co-location instances may appear globally in the whole research area(global co-location pattern),or appear in a local area of the research area(regional co-location pattern),Thus the multi-level co-location pattern mining is proposed.There are two problems with current multi-level co-location pattern mining methods:1)the existing multi-level co-location pattern mining methods ignore the spatial distribution characteristics of patterns and fail to accurately distinguish global and regional co-location patterns;2)the existing multi-level pattern mining method uses global non-prevalent co-location patterns as candidate regional co-location patterns,and the number of candidate patterns is too large.In response to the above problems,firstly,we define the uniform coefficient of the instance distribution of the co-location pattern and consider the pattern distribution in space while considering the pattern prevalence,so as to correctly and efficiently identify the global and regional co-location patterns.Secondly,a novel multi-level co-location pattern mining algorithm is designed based on the uniformity coefficient of the instance distribution of the pattern.In this algorithm,an effective pruning strategy is proposed to improve the efficiency of the algorithm.Finally,extensive experiments are carried out on real and synthetic data sets,which verify the correctness and efficiency of the proposed method.

关 键 词：空间数据挖掘 多级co-location模式 空间异质性 均匀系数 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]