Discovering High Influence Co-location Patterns from Spatial Data Sets

The co-location pattern is a subset of spatial features that are frequently located together in spatial proximity. However, the traditional approaches only focus on the prevalence of patterns, and it cannot reflect the influence of patterns. In this paper, we are committed to address the problem of mining high influence co-location patterns. At first, we define the concepts of influence features and reference features. Based on these concepts, a series of definitions are introduced further to describe the influence co-location pattern. Secondly, a metric is designed to measure the influence degree of the influence co-location pattern, and a basic algorithm for mining high influence co-location patterns is presented. Then, according to the properties of the influence co-location pattern, the corresponding pruning strategy is proposed to improve the efficiency of the algorithm. At last, we conduct extensive experiments on synthetic and real data sets to test our approaches. Experimental results show that our approaches are effective and efficient to discover high influence co-location patterns.