期刊
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
卷 42, 期 3, 页码 485-505出版社
SPRINGER
DOI: 10.1007/s10844-013-0280-5
关键词
Regional co-location pattern mining; kNNG; Variation coefficient
资金
- National Key Technologies R&D Program of China [2011BAD21B02]
- National Natural Science Foundation of China [61272303]
Spatial co-location pattern mining discovers the subsets of features of which the events are frequently located together in geographic space. The current research on this topic adopts a distance threshold that has limitations in spatial data sets with various magnitudes of neighborhood distances, especially for mining of regional co-location patterns. In this paper, we propose a hierarchical co-location mining framework accounting for both variety of neighborhood distances and spatial heterogeneity. By adopting k-nearest neighbor graph (kNNG) instead of distance threshold, we propose distance variation coefficient as a new measure to drive the mining operations and determine an individual neighborhood relationship graph for each region. The proposed mining algorithm outputs a set of regions with each of them an individual set of regional co-location patterns. The experimental results on both synthetic and real world data sets show that our framework is effective to discover these regional co-location patterns.
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