Characterizing activity patterns using co-clustering and user-activity network

Arian, Ali; Ermagun, Alireza; Chiu, Yi-Chang

doi:10.1109/itsc.2017.8317871

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Arian et al [21] used a co-clustering method to group users and activities from the origin-destination location of moving objects. Once it groups users instead of trajectories, the approach cannot find movement patterns in the trajectories.…”

Section: Related Workmentioning

confidence: 99%

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Mining frequency-based sequential trajectory co-clusters

Santos¹,

Carvalho²,

Bogorny³

2021

Preprint

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Co-clustering is a specific type of clustering that addresses the problem of finding groups of objects without necessarily considering all attributes. This technique has shown to have more consistent results in high-dimensional sparse data than traditional clustering. In trajectory co-clustering, the methods found in the literature have two main limitations: first, the space and time dimensions have to be constrained by user-defined thresholds; second, elements (trajectory points) are clustered ignoring the trajectory sequence, assuming that the points are independent among them. To address the limitations above, we propose a new trajectory co-clustering method for mining semantic trajectory co-clusters. It simultaneously clusters the trajectories and their elements taking into account account the order in which they appear. This new method uses the element frequency to identify candidate co-clusters. Besides, it uses an objective cost function that automatically drives the co-clustering process, avoiding the need for constraining dimensions. We evaluate the proposed approach using real-world a publicly available dataset. The experimental results show that our proposal finds frequent and meaningful contiguous sequences revealing mobility patterns, thereby the most relevant elements.

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Section: Related Workmentioning

confidence: 99%

“…Different trajectory co-clustering approaches were proposed to deal with trajectory data regarding the spatial dimension [19,4,3], space and time [20], or semantics [21,22,23]. However, these approaches have two main limitations.…”

Section: Introductionmentioning

confidence: 99%