Knfcom-t: A κ-nearest Features-based Co-location Pattern Mining Algorithm For Large Spatial Data Sets By Using T-trees

摘要

Spatial co-location patterns represent the subsets of Boolean spatial features whose instances often locate in close geographic proximity. The existing co-location pattern mining algorithms aim to find spatial relations based on the distance threshold. However, it is hard to decide the distance threshold for a spatial data set without any prior knowledge. Moreover, spatial data sets are usually not evenly distributed and a single distance value cannot fit an irregularly distributed spatial data set well. In this paper, we propose the notion of the κ-nearest features (simply κ-NF)-based co-location pattern. The κ-NF set of a spatial feature's instances is used to evaluate the spatial relationship between this feature and any other feature. A κ-NF-based co-location pattern mining algorithm by using T-tree (KNFCOM-T in short) is further presented to identify the co-location patterns in large spatial data sets. The experimental results show that the KNFCOM-T algorithm is effective and efficient and its complexity is O(n).