关键词: 空间数据挖掘, 空间co-location模式, 模糊邻近, 模糊聚类

Abstract: A spatial co-location pattern is a set of spatial features whose instances frequently appear together in a geographic space. The traditional co-location pattern mining usually determines the proximity relationship of two instances by giving a single proximity threshold. Using a single proximity threshold may result in the loss of the proximity relationships, and does not consider the influence of different distances on the proximity relationships. In addition, the efficiency of the algorithm is sensitive to the prevalence thresholds. Because frequently co-located features have a high degree of similarity, they can be clustered together by using clustering algorithms. Moreover, the proximity relationship and the co-located relationship of features are fuzzy concepts. Thus, based on the definitions of the fuzzy proximity relationship and the feature proximity measure, this paper studies fuzzy mining technique in spatial co-location pattern mining and proposes a method of mining the spatial co-location patterns by combining the fuzzy set theory and the clustering algorithm. The practicability, efficiency and robustness of the proposed method are proved by extensive experiments.

Key words: spatial data mining, spatial co-location pattern, fuzzy proximity relationship, fuzzy clustering