Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
