A novel outlier detection method for spatio-tempral trajectory data
ICHIT'11 Proceedings of the 5th international conference on Convergence and hybrid information technology
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As technology advances we encounter more available data on moving objects, which can be mined to our benefit. In order to efficiently mine this large amount of data we propose an enhanced segmentation algorithm for representing a periodic spatio-temporal trajectory, as a compact set of minimal bounding boxes (MBBs). We also introduce a new, "data-amount- based" similarity measure between mobile trajectories which is compared empirically to an existing similarity measure by clustering spatio-temporal data and evaluating the quality of clusters and the execution times. Finally, we evaluate the values of segmentation thresholds used by the proposed segmentation algorithm through studying the tradeoff between running times and clustering validity as the segmentation resolution increases.