| Popis: |
The spatial co-location pattern refers to a subset of non-empty spatial features whose instances are frequently located together in a spatial neighborhood. Researchers have carried out relevant research of top-k spatial co-location pattern mining for deterministic data and uncertain data, but there is no research on top-k average utility co-location pattern mining for fuzzy features. Therefore, this paper proposes top-k average utility co-location pattern mining for fuzzy features. Firstly, the relevant concepts of top-k average utility co-location patterns of fuzzy features are defined, and the “downward close” nature of the extended fuzzy average utility of the pattern is analyzed. Secondly, an algorithm of mining top-k average utility co-location patterns based on extended fuzzy average utility value is designed,solving the problem that the fuzzy average utility does not satisfy the “downward close” nature. Thirdly, a pruning method based on a locally extended fuzzy average utility is proposed, which effectively reduces the search space for top-k average utility co-location pattern mining, and further improves the efficiency of the mining algorithm. Finally, the practicability, efficiency and robustness of the proposed algorithm are verified on real and synthetic datasets. |
