Sequential pattern mining of geo-tagged photos with an arbitrary regions-of-interest detection method

Geo-tagged photos leave trails of movement that form trajectories. Regions-of-interest detection identifies interesting hot spots where many trajectories visit and large geo-tagged photos are uploaded. Extraction of exact shapes of regions-of-interest is a key step to understanding these trajectories and mining sequential trajectory patterns. This article introduces an efficient and effective grid-based regions-of-interest detection method that is linear to the number of grid cells, and is able to detect arbitrary shapes of regions-of-interest. The proposed algorithm is combined with sequential pattern mining to reveal sequential trajectory patterns. Experimental results reveal quality regions-of-interest and promising sequential trajectory patterns that demonstrate the benefits of our algorithm.