The Implementation of Hesitant Fuzzy Spatial Co-Location Pattern Mining Algorithm Based on Python

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.