Analysis of Sparse Trajectory Features Based on Mobile Device Location for User Group Classification Using Gaussian Mixture Model

Yohei Kakimoto; Yuto Omae; Hirotaka Takahashi; Hirotaka TAKAHASHI

doi:10.3390/app15020982

Analysis of Sparse Trajectory Features Based on Mobile Device Location for User Group Classification Using Gaussian Mixture Model

Translated title of the contribution: Analysis of Sparse Trajectory Features Based on Mobile Device Location for User Group Classification Using Gaussian Mixture Model

Yohei Kakimoto
, Yuto Omae
, Hirotaka Takahashi
, Hirotaka TAKAHASHI

Department Of Design And Data Science

Research output: Contribution to journal › Article › peer-review

Translated title of the contribution	Analysis of Sparse Trajectory Features Based on Mobile Device Location for User Group Classification Using Gaussian Mixture Model
Original language	Japanese
Pages (from-to)	982 -
Journal	Applied Sciences
Volume	15
Issue number	2
DOIs	https://doi.org/10.3390/app15020982
State	Published - 20 Jan 2025

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10.3390/app15020982

Nihon University Researchers Discuss Research in Applied Sciences (Analysis of Sparse Trajectory Features Based on Mobile Device Location for User Group Classification Using Gaussian Mixture Model)
TAKAHASHI, H.
11/02/25
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Cite this

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title = "Analysis of Sparse Trajectory Features Based on Mobile Device Location for User Group Classification Using Gaussian Mixture Model",

author = "\{Yohei Kakimoto\} and \{Yuto Omae\} and \{Hirotaka Takahashi\} and Hirotaka TAKAHASHI",

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language = "Japanese",

volume = "15",

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AU - Yohei Kakimoto, null

AU - Yuto Omae, null

AU - Hirotaka Takahashi, null

AU - TAKAHASHI, Hirotaka

PY - 2025/1/20

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VL - 15

SP - 982 -

JO - Applied Sciences

JF - Applied Sciences

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Analysis of Sparse Trajectory Features Based on Mobile Device Location for User Group Classification Using Gaussian Mixture Model

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Nihon University Researchers Discuss Research in Applied Sciences (Analysis of Sparse Trajectory Features Based on Mobile Device Location for User Group Classification Using Gaussian Mixture Model)

Cite this