Spatiotemporal event sequences (STESs) are the ordered series of event types whose spatiotemporal instances frequently follow each other in time and are located close-by. Previous studies on STES mining require significance and prevalence thresholds for the discovery, which is often a very difficult even for domain experts. As the quality of the discovered STESs is of great importance to the domain experts who use these algorithms, we introduce novel STES mining algorithms to discover the most relevant STESs without significance and prevalence thresholds. Our algorithms uses statistical bootstrapping and top-K most prevalent spatiotemporal event sequences from top-R% most significant follow relationships.
For more information, visit the link to the project: https://grid.cs.gsu.edu/~baydin2/proj/stes.html