Mining sequential movement patterns describing group behaviour in potentially streaming spatio-temporal data sets is a challenging problem. Movements are typically noisy and often overlap each other. This makes a set of simple patterns difficult to interpret and sequences difficult to mine. Furthermore, group behaviour is complex. Objects in a group may behave similarly for a period of time (an interesting pattern sequence), then split up – either spatially, temporally or both; making a series of uninteresting movements before rejoining again. This behaviour must be captured in a single pattern for that group, rather than a number of unconnected pattern sequences. Secondly, it often occurs that individual objects only move along segments of a path, perhaps between intersections in a road or highway. However, the entire path is interesting when all such behaviours are taken together. Therefore, a pattern describing such behaviour should be found, rather than just a number of short sequences. This paper solves these challenges, among others, by mining sequences of Spatio-Temporal Association Rules. Theoretical results are exploited in order to develop an efficient algorithm, which is demonstrated to have linear run time in the number of interesting sequences discovered. A lattice for drill down and roll up exploratory analysis of the sequence patterns is proposed. Finally, verifiable and interesting patterns possessing the above characteristics are found in a real world animal tracking data set. 1

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regression-based approach for mining user movement patterns from random sample data