Spatial Co-location Pattern Mining Based on Density Peaks Clustering and Fuzzy Theory
Spatial co-location patterns are the subsets of spatial features whose instances are frequently located together in geographic space. Traditional co-location pattern mining framework usually determines the proximity relationship of spatial instances by a user-specific distance threshold. However, in real life, the proximity relationship is a fuzzy concept and difficult to measure only by an absolute distance threshold. Furthermore, the spatial clique generating process consumes huge computational and spatial costs. In this paper, we propose a new framework for mining co-location patterns based on density peaks clustering and fuzzy theory. The experiments show that our method performs more efficient than the traditional Join-less method and the mining results on two real-world data sets indicate our method is significant and practical.
KeywordsSpatial co-location pattern Proximity relationship Fuzzy theory Density peaks clustering
This work is supported by the National Natural Science Foundation of China (61472346, 61662086, 61762090), the Natural Science Foundation of Yunnan Province (2015FB114, 2016FA026), and the Project of Innovative Research Team of Yunnan Province.
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