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Mining human periodic behaviors using mobility intention and relative entropy

conference contribution
posted on 2018-06-19, 00:00 authored by F Yi, L Yin, H Wen, H Zhu, L Sun, Gang LiGang Li
Human periodic behaviors is essential to many applications, and many research work show that human behaviors are periodic. However, existing human periodic works are reported with limited improvements in using periodicity of locations and unsatisfactory accuracy for oscillation of human periodic behaviors. To address these challenges, in this paper we propose a Mobility Intention and Relative Entropy (MIRE) model. We use mobility intentions extracting from dataset by tensor decomposition to characterize users’ history records, and use sub-sequence of same mobility intention to mine human periodic behaviors. A new periodicity detection algorithm based on relative entropy is then proposed. The experimental results on real-world datasets demonstrate that the proposed MIRE model can properly mining human periodic behaviors. The comparison results also indicate that MIRE model significantly outperforms state-of-the-art periodicity detection algorithms.

History

Volume

10937

Pagination

488-499

Location

Melbourne, Victoria

Start date

2018-06-03

End date

2018-06-06

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783319930336

Language

eng

Publication classification

E Conference publication, E1 Full written paper - refereed

Editor/Contributor(s)

Phung D, Tseng V, Webb G, Ho B, Ganji M, Rashidi L

Title of proceedings

PAKDD 2018 : Advances in Knowledge Discovery and Data Mining : Proceedings of 22nd Pacific-Asia Conference

Event

Knowledge Discovery and Data Mining. Pacific-Asia Conference (22nd : 2018 : Melbourne, Victoria)

Publisher

Springer

Place of publication

Cham, Switzerland

Series

Lecture Notes in Computer Science