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.
@inproceedings{DBLP:conf/percom/NguyenNSP16,
author = {Nguyen Cong Thuong and
Vu Nguyen and
Flora D. Salim and
Dinh Q. Phung},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/conf/percom/NguyenNSP16.bib},
booktitle = {2016 IEEE International Conference on Pervasive Computing and Communications,
PerCom 2016, Sydney, Australia, March 14-19, 2016},
doi = {10.1109/PERCOM.2016.7456501},
pages = {1--9},
publisher = {IEEE Computer Society},
timestamp = {Wed, 16 Oct 2019 14:14:54 +0200},
title = {SECC: Simultaneous extraction of context and community from pervasive
signals},
url = {https://doi.org/10.1109/PERCOM.2016.7456501},
year = {2016}
}
© 2021 Flora Salim - CRUISE Research Group.