Abstract
Abstract. As one of the important research directions in the spatial data mining, spatial co-location pattern mining aimed at finding the spatial features whose the instances are frequent co-locate in neighbouring domain. With the introduction of fuzzy sets into traditional spatial co-location pattern mining, the research on fuzzy spatial co-location pattern mining has been deepened continuously, which extends traditional spatial co-location pattern mining to deal with fuzzy spatial objects and discover their laws of spatial symbiosis. In this paper, the operation principle of a classical join-based algorithm for mining spatial co-location patterns is briefly described firstly. Then, combining with the definition of classical participation rate and participation degree, a novel hesitant fuzzy spatial co-location pattern mining algorithm is proposed based on the establishment of the hesitant fuzzy participation rate and hesitant fuzzy participation formula according to the characteristics in fusion of hesitant fuzzy set theory, the score function and spatial co-location pattern mining. Finally, the proposed algorithm is written and implemented based on Python language, which uses a NumPy system to the expansion of the open source numerical calculation. The Python program of the proposed algorithm includes the method of computing hesitant fuzzy membership based on score function, the implementation of generating k-order candidate patterns, k-order frequent patterns and k-order table instances. A hesitant fuzzy spatial co-location pattern mining experiment is carried out and the experimental results show that the proposed and implemented algorithm is effective and feasible.
Highlights
Spatial co-location pattern mining is one of the important research directions of spatial data mining, which was first proposed in 2001 (Shekhar, Huang, 2001)
By combining with spatial co-location pattern mining and introducing the calculation method of the score function (Xia, Xu, 2011), we establish relevant definitions mining strategies and algorithms, and obtain a spatial colocation pattern mining method based on hesitant fuzzy sets
Definition 5: Set fi as a spatial object and HFS_S_PR(c, fi) represents PR in score function based on hesitation fuzzy sets of fi in the k-order space co-location mode c, it is the ratio between the sum of the score of the non-recurring location fuzzy instance in the table instance of the space co-location mode c and the total number of instances in fi
Summary
Spatial co-location pattern mining is one of the important research directions of spatial data mining, which was first proposed in 2001 (Shekhar, Huang, 2001). In a certain extent, the inaccurate intuitive judgment like this can reflect the position of the object and the relationship between the object and the object. Such description is one of the main research contents of fuzzy mathematics (Xie, 2000). The hesitant fuzzy set allows the membership degree of each object to have multiple possible fuzzy elements, more accurately depicting the embarrassment of the decision-maker's dilemma. The spatial colocation model based on the hesitant fuzzy set can scientifically solve the problems such as the uncertainty of the location of point space object instances and the possibility of multiple values of point space object instances. By combining with spatial co-location pattern mining and introducing the calculation method of the score function (Xia, Xu, 2011) , we establish relevant definitions mining strategies and algorithms, and obtain a spatial colocation pattern mining method based on hesitant fuzzy sets
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