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In this paper, we propose an algorithm that extracts spatial frequentpatterns to explain the relative characteristics of a specific location fromthe available social data. This paper proposes a spatial social data modelwhich includes spatial social data, spatial support, spatial frequent patterns,spatial partition, and spatial clustering; these concepts are used fordescribing the exploration algorithm of spatial frequent patterns. With thesedefined concepts as the foundation, an SFP-tree structure that maintains notonly the frequent words but also the frequent cells was proposed, and anSFP-growth algorithm that explores the frequent patterns on the basis of thisSFP-tree was proposed.

Fast Mining of Spatial Frequent Wordset from Social Database