Mining frequent trajectory patterns from online footprints

Abstract

Trajectory pattern mining has been performed over many datasets, including animal movement, GPS trajectories, and human travel history. This paper aims to explore and mine individual frequently visited regions and trajectory patterns using online footprints captured through a social media site (i.e., Twitter). Regions of frequent visits representing daily activity areas at which an individual appears are derived using the DBSCAN clustering algorithm. A trajectory pattern mining algorithm is then applied to discover ordered sequences of these spatial regions that the individual visits frequently. To illustrate and test the effectiveness of the proposed methods, we analyze the activity patterns of a selected Twitter user using the geo-tagged tweets posted by the user for an extended period. The preliminary assessment indicates that our approach can be applied to mine individual frequent trajectory patterns from online footprints that are of relatively low and irregular spatial and temporal resolutions.