Mining regional co-location patterns with kNNG

作者：Qian, Feng^*; Chiew, Kevin; He, Qinming; Huang, Hao

来源：Journal of Intelligent Information Systems, 2014, 42(3): 485-505.

DOI：10.1007/s10844-013-0280-5

• Regional co-location pattern mining
• kNNG
• Variation coefficient

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.

• 出版日期2014-6
• 单位浙江大学