Clustering and aggregating clues of trajectories for mining trajectory patterns and routes

Clustering and aggregating clues of trajectories for mining trajectory patterns and routes In this paper, we propose a new trajectory pattern mining framework, namely Clustering and Aggregating Clues of Trajectories (CACT) , for discovering trajectory routes that represent the 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 the movements of users, which bring many challenging issues to trajectory pattern mining. 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. Finally, we devise the clue-aware trajectory aggregation algorithm to aggregate trajectories in the same group to derive the corresponding trajectory pattern and route. We validate our ideas and evaluate the proposed CACT framework by experiments using both synthetic and real datasets. The experimental results show that CACT is more effective in discovering trajectory patterns than the state-of-the-art techniques for mining trajectory patterns. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Clustering and aggregating clues of trajectories for mining trajectory patterns and routes

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Publisher
Springer Journals
Copyright
Copyright © 2015 by Springer-Verlag Berlin Heidelberg
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-011-0262-6
Publisher site
See Article on Publisher Site

Abstract

In this paper, we propose a new trajectory pattern mining framework, namely Clustering and Aggregating Clues of Trajectories (CACT) , for discovering trajectory routes that represent the 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 the movements of users, which bring many challenging issues to trajectory pattern mining. 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. Finally, we devise the clue-aware trajectory aggregation algorithm to aggregate trajectories in the same group to derive the corresponding trajectory pattern and route. We validate our ideas and evaluate the proposed CACT framework by experiments using both synthetic and real datasets. The experimental results show that CACT is more effective in discovering trajectory patterns than the state-of-the-art techniques for mining trajectory patterns.

Journal

The VLDB JournalSpringer Journals

Published: Apr 1, 2015

References

  • Time-focused clustering of trajectories of moving objects
    Nanni, M.; Pedreschi, D.

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