Clustering and Aggregating Clues of Trajectories for Trajectory Pattern Mining

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Title:	Clustering and Aggregating Clues of Trajectories for Trajectory Pattern Mining
Authors:	Hung, Chih-Chieh;Peng, Wen-Chih;Lee, Wang-Chien
Keywords:	Trajectory pattern mining;Trajectory similarity;Trajectory clustering
Date:	2015-04-01
Issue Date:	2016-04-22 13:41:56 (UTC+8)
Publisher:	A C M Special Interest Group
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.
Relation:	VLDB JOURNAL 24(2), pp.162-192
Appears in Collections:	[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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