SECC: simultaneous extraction of context and community from pervasive signals

Nguyen, Thuong, Nguyen, Vu, Salim, Flora D. and Phung, Dinh 2016, SECC: simultaneous extraction of context and community from pervasive signals, in PerCom 2016: Proceedings of the 14th IEEE International Conference on Pervasive Computing and Communications, IEEE, Piscataway, N.J., pp. 1-9, doi: 10.1109/PERCOM.2016.7456501.

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Title SECC: simultaneous extraction of context and community from pervasive signals
Author(s) Nguyen, Thuong
Nguyen, Vu
Salim, Flora D.
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Conference name IEEE International Conference on Pervasive Computing and Communications (14th : 2016 : Sydney, N.S.W.)
Conference location Sydney, N.S.W.
Conference dates 14-19 Mar. 2016
Title of proceedings PerCom 2016: Proceedings of the 14th IEEE International Conference on Pervasive Computing and Communications
Publication date 2016
Start page 1
End page 9
Total pages 9
Publisher IEEE
Place of publication Piscataway, N.J.
Summary Understanding user contexts and group structures plays a central role in pervasive computing. These contexts and community structures are complex to mine from data collected in the wild due to the unprecedented growth of data, noise, uncertainties and complexities. Typical existing approaches would first extract the latent patterns to explain the human dynamics or behaviors and then use them as the way to consistently formulate numerical representations for community detection, often via a clustering method. While being able to capture high-order and complex representations, these two steps are performed separately. More importantly, they face a fundamental difficulty in determining the correct number of latent patterns and communities. This paper presents an approach that seamlessly addresses these challenges to simultaneously discover latent patterns and communities in a unified Bayesian nonparametric framework. Our Simultaneous Extraction of Context and Community (SECC) model roots in the nested Dirichlet process theory which allows nested structure to be built to explain data at multiple levels. We demonstrate our framework on three public datasets where the advantages of the proposed approach are validated.
ISBN 9781467387798
Language eng
DOI 10.1109/PERCOM.2016.7456501
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2016, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083608

Document type: Conference Paper
Collections: Centre for Pattern Recognition and Data Analytics
2018 ERA Submission
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