Spatial Colocation Pattern Discovery Incorporating Fuzzy Theory

Abstract

Spatial colocation patterns are subsets of spatial feature sets that are frequently “neighboring” in space. In the majority of existing mining methods, neighboring is determined by a single distance threshold and does not take the neighboring degree into consideration causing many neighbor relationships to be lost and they also cannot objectively describe the correlation between spatial features. In addition, existing methods ignore the instance-sharing problem of spatial neighbor relationships when calculating the prevalence of colocation patterns. In order to overcome these weaknesses, this article introduces the fuzzy theory into the spatial colocation pattern discovery. Through the fuzziness of spatial neighbor relationships between spatial instances, we develop a reasonable fuzzy proximity metric, which takes the instance-sharing problem into account and which measures the similarity between different spatial features. Three effective fuzzy clustering methods are proposed, which ensure that colocation patterns can be extracted accurately and quickly. Finally, extensive experiments on five synthetic datasets and five real-world datasets prove the practicability and efficiency of the proposed methods.