Mining spatio-temporal association rules, sources, sinks, stationary regions and thoroughfares in object mobility databases

As mobile devices proliferate and networks become more location-aware, the corresponding growth in spatio-temporal data will demand analysis techniques to mine pat-terns that take into account the semantics of such data. As-sociation Rule Mining (ARM) has been one of the more extensively studied data mining techniques, but it consid-ers discrete transactional data (supermarket or sequential). Most attempts to apply this technique to spatial-temporal domains maps the data to transactions, thus losing the spatio-temporal characteristics. We provide a comprehen-sive definition of spatio-temporal association rules (STARs) that describe how objects move between regions over time. We define support in the spatio-temporal domain to effec-tively deal with the semantics of such data. We also intro-duce other patterns that are useful for mobility data; sta-tionary regions and high traffic regions. The latter con-sists of sources, sinks and thoroughfares. These patterns describe important temporal characteristics of regions and we show that they can be considered as special STARs. We provide efficient algorithms to find these patterns. Partic-ularly, via several pruning properties we can mine STARs efficiently by first mining high traffic regions.