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2016 IEEE International Conference on Pervasive Computing and Communications (PerCom) (2016)
Sydney, Australia
March 14, 2016 to March 19, 2016
ISBN: 978-1-4673-8778-1
pp: 1-9
Thuong Nguyen , Computer Science and Information Technology, RMIT University, Australia
Vu Nguyen , Centre for Pattern Recognition and Data Analytics, Deakin University, Australia
Flora D. Salim , Computer Science and Information Technology, RMIT University, Australia
Dinh Phung , Centre for Pattern Recognition and Data Analytics, Deakin University, Australia
ABSTRACT
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 highorder 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.
INDEX TERMS
Context, Data mining, Feature extraction, Context modeling, Data models, Mixture models, Bluetooth
CITATION

T. Nguyen, V. Nguyen, F. D. Salim and D. Phung, "SECC: Simultaneous extraction of context and community from pervasive signals," 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)(PERCOM), Sydney, Australia, 2016, pp. 1-9.
doi:10.1109/PERCOM.2016.7456501
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