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Fuzzy Instance Space Co-Location Pattern Mining Algorithm Based on Voronoi Graph and Distance Attenuation Effect

DOI: 10.12677/hjdm.2024.142006, PP. 65-80

Keywords: 空间并置模式,模糊实例,距离衰减效应,Voronoi图
Space Co-Location Pattern
, Fuzzy Instance, Distance Attenuation Effect, Voronoi Diagram

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Space co-location pattern mining is used to discover a set of spatial features whose instances are frequently adjacent to each other in space. In the process of traditional space co-location pattern mining, spatial instances are abstracted into point objects, and each instance corresponds to a definite location. However, large spatial examples have multiple important location points (such as hospital, and park entrance), and their spatial locations are different due to different cognition of their entrances, which is fuzzy. For these fuzzy instances, this paper considers the contribution of important location points to the instance scale, and redefines the proximity between the instances. In addition, the traditional collocation pattern mining method ignores the spatial distribution density of feature instances and the proximity degree between neighboring instances, and uses static distance threshold to identify neighboring instances. In this paper, considering the distribution density of features, Voronoi diagram is used to adaptively extract adjacent instances with different features, and the proximity of adjacent instances is described more scientifically by combining the distance decay function of adjacent instances. In this paper, a space co-location pattern mining method is proposed, which takes into account both the fuzzy instance size and distance attenuation effects. In order to realize fast mining, a mining framework for the participating instances of maximal clique and hash table search is designed. Experiments are carried out on real data sets and synthetic data sets to verify that the proposed algorithm can find meaningful patterns ignored by traditional space co-location pattern mining methods.


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