Clustering clues of trajectories for discovering frequent movement behaviors

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 Scopus citations


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.

Original languageEnglish
Title of host publicationBehavior Computing
Subtitle of host publicationModeling, Analysis, Mining and Decision
PublisherSpringer-Verlag London Ltd
Number of pages18
ISBN (Electronic)9781447129691
ISBN (Print)9781447129684
StatePublished - 1 Jan 2012


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