On a clustering-based mining approach with labeled semantics for significant place discovery

With the rapid increase in GPS data collection through pervasive use of mobile devices, it has become an important problem to discover significant places of moving objects from complex spatial and temporal trajectories. This problem is challenging mainly because such trajectory data suffer from several issues including incompleteness, low quality, high redundancy, and oftentimes trajectory points do not follow Gaussian distribution. We propose a clustering-based method with temporal and spatial semantics, referred to as Stops and Moves of Trajectories using Attribute Selection (SMoTAS), whose technical advantages are multifold. Firstly, it improves data availability by using a self-adaptive algorithm to correct the deviation in traditional speed-based methods. Secondly, it improves place mining accuracy by filtering multi-label clustering results when there is a lack of detailed geographic data. Thirdly, it employs feature selection to exploit the core attributes of clustering and simplify the clustering results with Grubbs criterion. Experimental results on real-life datasets show that SMoTAS not only achieves substantial improvement of accuracy over existing methods in discovering significant places, but also exhibits superior adaptability to different trajectories and application scenarios.