A spatial co-location pattern mining approach based on column calculation

Abstract

Spatial co-location pattern mining aims to discover correlations between spatial features. A co-location pattern corresponds to a subset of spatial features whose instances are frequently located in spatial neighborhoods. Most co-location pattern mining approaches calculate the prevalence based on table instances, but the time and space costs of generating table instances are enormous. To address this problem, this paper presents a novel co-location pattern mining approach based on column calculation, which only searches for participating instances of features in a pattern without having to generate the table instance. Furthermore, this paper designs the instance search space pruning, the candidate participating instance verification, and the prevalence beforehand awareness technologies to speed up the search of participating instances. The CPM-Col method is proposed on this basis, and its complexity, accuracy, and completeness are addressed. Extensive experiments are conducted on real and synthetic datasets, and experimental results show that the CPM-Col algorithm has better performance and scalability than seven other baseline algorithms, especially with a performance gain of several times or even orders of magnitude. Moreover, the effectiveness of the proposed optimization strategies is verified experimentally.