Mining Frequent Spatio-Temporal Items in Trajectory Data
نویسندگان
چکیده
The time aspect is not currently taken into account for finding a region of interesting (ROI) or a hot region, so that due to the time to visit frequently a place cannot be determined, it is difficult to discover the visiting regularity for a moving object. To this end, the spatio-temporal item (STI) and frequent spatio-temporal item (FSTI) integrated spatial and temporal attributes are defined. The FSTIs can represent a moving object often visits which area in what time, which can provide more useful information to improve the level of the location-based services(LBS). In order to find FSTIs, STIs are generated by using a density-based clustering algorithm to recognize the stay regions of objects, and then the STIs are mapped to 3D-grids integrated spatial and temporal dimensions. Finally, the extraction merger strategy is used on the frequent grid cells to recombine the FSTIs. Experimental results on real dataset show that the approach proposed for mining FSTIs is effective.
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