Efficiently mining spatial co-location patterns utilizing fuzzy grid cliques

Abstract

Spatial co-location pattern (SCP) mining discovers subsets of spatial feature types whose objects frequently co-locate in a geographic space. Many existing methods treat the space as homogeneous, use absolute Euclidean distance to measure the neighbor relationship between objects and use a participation index to measure the prevalence of SCPs. Several issues arise: (1) it may be that the distance between objects cannot be accurately defined since it is a relative and fuzzy concept; (2) the degree of neighborliness and sharing relationships between objects are neglected; (3) current methods for collecting participating objects by generating candidate table instances utilizing combined search techniques are computationally expensive. In this paper, we propose a method based on fuzzy grid cliques to find all prevalent SCPs. Specifically, fuzzy theory is introduced to define the proximity between objects. The fuzzy participating contribution index (FPCI) is defined to measure the prevalence of SCPs, and it considers both the neighbor degree and sharing relationship between objects. Based on the defined proximity, a basic mining framework based on fuzzy grid cliques is proposed. We first design a naive algorithm based on the participating objects’ filtering and verification called POFV, which uses a fuzzy grid clique search technology instead of combination search to collect participating objects and avoids enumerating all table instances. To solve a dilemma within POFV, we develop a maximal fuzzy grid cliques search based algorithm called MFGC, which can effectively reuse information. Experiments on both real and synthetic data sets verify the superiority of our proposed approaches, by showing that MFGC greatly outperforms the baseline algorithm and more efficiently captures SCPs.