A spatial co-location pattern mining framework insensitive to prevalence thresholds based on overlapping cliques

• DOI: 10.1007/s10619-021-07333-2
• Publication Date: 2021-03-18T18:47:37Z
• Authors: Vanha Tran; Lizhen Wang; Lihua Zhou
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; 16. Peace & justice

Discovering spatial co-location patterns is a process of finding groups of distinct spatial features whose instances are frequently located together in spatial proximity. A co-location pattern is prevalent if its participation index is no less than a minimum prevalence threshold given by users. Most of the existing algorithms are very sensitive to the prevalence threshold, when users change the prevalence threshold, these algorithms have to re-collect table instances and re-calculate participation indexes of all patterns for mining the prevalent patterns that users expect to acquire. To tackle this issue, we propose an overlapping clique-based spatial co-location pattern mining framework (OCSCP). In our framework, we design a two-level filter mechanism with the first level is a feature type filter and the second level is a neighboring instance filter. By employing the mechanism, under a certain neighbor relationship, spatial instances are divided into a set of overlapping cliques and each clique is also a co-location instance of a pattern. And then, a co-location pattern hash map structure is designed to store table instances of patterns based on these overlapping cliques. The participation index of each pattern can be fast and directly calculated from the hash map structure. Thus, when the prevalence threshold is changed, the proposed framework does not need to re-gather table instances, and the mining result can be adaptively and quickly given to users. The proposed algorithms are performed on both synthetic and real-world data sets to demonstrate that our algorithms can rapidly respond to user requirements compared to the previous algorithms.