Mining fuzzy sub-prevalent co-location pattern with dominant feature
Kaifang Xiong, Hongmei Chen, Lizhen Wang, Qing Xiao · Proceedings of the 30th International Conference on Advances in Geographic Information Systems · 2022
Prevalent co-location pattern mining aims to find a subset of spatial features whose instances are frequently located together in geo-space. Sub-prevalent co-location pattern mining attains co-location patterns with richer spatial relationships based on star instance model instead of clique instance model. Further, discovering dominant features in a sub-prevalent co-location pattern is important to reveal the interaction relationship between spatial features and improve the application of the pattern. However, the methods for mining sub-prevalent co-location pattern with dominant feature use a distance threshold to determine the binary neighbor relationship between spatial instances, and ignore the fuzziness of neighbor relationship which can give a rise to the fuzziness of dominance relationship between spatial features. Thus, this paper presents mining fuzzy sub-prevalent co-location pattern with dominant feature by exploring spatial instance distribution based on the fuzzy theory. Specifically, a novel pattern, fuzzy sub-prevalent co-location pattern with dominant feature, is proposed by defining the fuzzy neighbor relationship between spatial instances and the fuzzy dominant relationship between spatial features. Then, an efficient algorithm to mine the proposed patterns is designed by utilizing the anti-monotonicity of fuzzy star participation index and fuzzy star dominance index to prune unpromising patterns. The experimental results show that the proposed patterns are practical and the mining algorithm is efficient.