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Mining spatial co-location pattern is a challenging and essential task in spatial data mining, which is aimed to discover the subsets of spatial features frequently observed together in the adjacent geographic space. In this paper, we apply the concept of fuzzy set theory to the high utility co-location pattern mining, which allows one to find all the high utility co-location patterns from fuzzy datasets. Firstly, we define the related concepts, including the utility of fuzzy pattern and its ratio. Secondly, we propose an efficient fuzzy high utility co-location mining basic algorithm and the optimization one. The latter algorithm constructs the star row-instance weighted utility downward closure property, which can significantly improve the efficiency and running time of finding the patterns. Finally, the feasibility of proposed algorithms is experimentally verified using synthetic and real datasets.
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