Affiliations: [a] School of Computer Science and Engineering, Shenyang Jianzhu University, Shenyang, Liaoning, China | [b] School of Design and Art, Shenyang Jianzhu University, Shenyang, Liaoning, China | [c] School of Engineering Training and Innovation, Shenyang Jianzhu University, Shenyang, Liaoning, China
Correspondence:
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Corresponding author: Siqi Niu, School of Design and Art, Shenyang Jianzhu University, Shenyang, Liaoning 110168, China. E-mail: [email protected].
Abstract: With the rapid development and widespread adoption of wearable technology, a new type of lifelog data is being collected and used in numerous studies. We refer to these data as informative lifelog which usually contain GPS, images, videos, text, etc. GPS trajectory data in lifelogs is typically categorized into continuous and discrete trajectories. Finding a point of interest (POI) from discrete trajectories is a challenging task to do and has caught little attention so far. This paper suggests an LP-DBSCAN model for mining personal trajectories from discrete GPS trajectory data. It makes use of the hierarchical structure information implied in GPS trajectory data and it is suggested a variable-levels, variable-parameters clustering method (LP-DBSCAN) based on the DBSCAN algorithm to increase the precision of finding POI information. Finally, the Liu lifelog dataset is subjected to a systematic evaluation. In terms of GPS data that are not evenly distributed geographically, the experimental results demonstrated that the proposed algorithm could more accurately identify POI information and address the adverse effects caused by the global parameters of the traditional DBSCAN algorithm.
Keywords: Personal big data, lifelog, points of interest, discrete trajectory, DBSCAN
