Clustering clues of trajectories for discovering frequent movement behaviors

Chih Chieh Hung*, Ling Yin Wei, Wen-Chih Peng


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3 引文 斯高帕斯(Scopus)


In this chapter, we present a new trajectory pattern mining framework, namely, Clustering Clues of Trajectories (CCT), for discovering trajectory routes that represent frequent movement behaviors of a user. In addition to spatial and temporal biases, we observe that trajectories contain silent durations, i.e., the time durations when no data points are available to describe movements of users, which bring many challenge issues in clustering trajectories. We claim that a movement behavior would leave some clues in its various sampled/observed trajectories. These clues may be extracted from spatially and temporally co-located data points from the observed trajectories. Based on this observation, we propose clue-aware trajectory similarity to measure the clues between two trajectories. Accordingly, we further propose the clue-aware trajectory clustering algorithm to cluster similar trajectories into groups to capture the movement behaviors of the user. We validate our ideas and evaluate the proposed CCT framework by experiments using both synthetic and real datasets. Experimental results show that CCT is more effective in discovering trajectory patterns than the state-of-the-art techniques in trajectory clustering.

主出版物標題Behavior Computing
主出版物子標題Modeling, Analysis, Mining and Decision
發行者Springer-Verlag London Ltd
出版狀態Published - 1 1月 2012


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