A regression-based approach for mining user movement patterns from random sample data
Data & Knowledge Engineering
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A Spatio-Temporal Association Rule (STAR) describes how objects move between regions over time. Since they describe only a single movement between two regions, it is very difficult to see larger patterns in the dataset by considering only the set of STARs. It is especially difficult on complex datasets where the underlying patterns overlap. At best we will miss important patterns - being unable to "see the forest for the trees", and at worst this can lead to false interpretations. We introduce the k-STAR pattern which describes the sequences of STARs that objects obey. Since a k-STAR captures sequences of object movements it solves these problems. We also allow space and time gaps between successive STARs, as well as supporting "replenishable' k-STARs so we are able to capture the rich set of patterns that exist in real world data. We define two important measures; min-l-support and min-l-confidence that allow us to achieve the above and present various antimonotonic and weakly anti-monotonic properties for reducing the search space.