Discovering Association Patterns in Large Spatio-temporal Databases

Over the past few years, a considerable number of studies have been made on market basket analysis. Market basket analysis is a useful method for discovering customer purchasing patterns by extracting association from stores´ transaction databases. In many business of today, customer transactions can be made in many different geographical locations round the clock, especially after e-business have become prevalent. The traditional methods that consider only the association rules of an individual location or all locations as a whole are not suitable for such a multi-location environment. We design a novel and efficient algorithm for mining spatio-temporal association rules which have multi-level time and location granularities, in spatio-temporal databases. Experimental results have shown that our methods are efficient and we can find spatio-temporal association rules satisfactorily