关键词: 网格微分算法, 质心, &sigma, ²微分格, 空间实例压缩率

Abstract: Spatial co-location patterns mining is an important task in spatial data mining, but the efficiencies of running time and space are low for traditional mining algorithms of determination data and uncertain data, not to mention the massive data. Therefore, based on the analysis of why traditional mining algorithms consumed excessive time and space resources, this paper proposes a grid differential algorithm to mine spatial co-location patterns. The new algorithm divides the traditional grids into differential ones, and then calculates the centroids of instances that belong to the same feature for each differential grid. Finally, based on these centroids, the co-location patterns are mined with multiresolution pruning method. The proposed algorithm greatly improves the overall efficiency and has a high accuracy rate, which better solves the problem of mining spatial co-location patterns from a massive data set. Extensive experiments show that the grid differential algorithm has the advantages of high efficiency, robustness and high accuracy and so on.

Key words: grid differential algorithm, centroid; σ² differential grid, compression ratio of spatial instances