A novel frequent trajectory mining method based on GSP

Authors:
Junhuai Li;Jinqin Wang;Lei Yu;Jing Zhang
Affiliations:
School of Computer Science & Engineering, Xi'an University of Technology, Xi'an, China;School of Computer Science & Engineering, Xi'an University of Technology, Xi'an, China;School of Computer Science & Engineering, Xi'an University of Technology, Xi'an, China;School of Computer Science & Engineering, Xi'an University of Technology, Xi'an, China
Venue:
WISM'11 Proceedings of the 2011 international conference on Web information systems and mining - Volume Part I
Year:
2011

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Abstract

With the development and popularity of various location technologies (GPS, Wireless cellular networks and etc.), people can easily access the location information of moving objects and use a variety of location-based services. In this paper, based on the feature that the location information of moving object is consecutive, we introduce the continuity in temporal and spatial as a constraint into the Sequential Pattern Mining algorithm GSP (Generalized Sequential Patterns) [3,4], and to mine frequent trajectories, and then display them in Google maps. We evaluated our method by using a large GPS dataset in real world and verified the feasibility and effectiveness of Sequential Pattern Mining algorithm in mining the frequent trajectories of multiple moving objects.