Research on Algorithms of Positive and Negative Co-occurrence in Spatio-temporal Datasets

Abstract

Discovering spatio-temporal co-occurrence patterns is a significant issue in many fields. Previous algorithms simply looked for positive patterns when mining spatial co-occurrence patterns. However, patterns with strong negative associations are ignored. This paper proposed a novel algorithm for mining both positive and negative co-occurrence patterns. We introduced the notions of positive and negative co-occurrence patterns, and positive and negative co-occurrence patterns are mined by using an effective pruning strategy. This paper analyzed the completeness and correctness of the algorithm. We conducted experiments using both real and synthetic data sets to validate the effectiveness and efficiency of the suggested method.