A Simultaneous Extraction of Context and Community from pervasive signals using nested Dirichlet process

Thuong Nguyen (Corresponding Author), Vu Nguyen, Flora Salim, L Duc Le Viet Duc, Dinh Phung

Research output: Contribution to journalArticleAcademicpeer-review

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 human dynamics or behaviors and then use them as a 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 a nested structure to be built to summarize data at multiple levels. We demonstrate our framework on five datasets where the advantages of the proposed approach are validated.
Original languageEnglish
Pages (from-to)396-417
Number of pages22
JournalPervasive and Mobile Computing
Volume38
DOIs
Publication statusPublished - Jul 2017

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Dirichlet Process
Ubiquitous computing
Community Detection
Bayesian Nonparametrics
Community Structure
Pervasive Computing
Clustering Methods
Roots
Higher Order
Uncertainty
Demonstrate
Community
Context
Framework
Model

Cite this

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A Simultaneous Extraction of Context and Community from pervasive signals using nested Dirichlet process. / Nguyen, Thuong (Corresponding Author); Nguyen, Vu; Salim, Flora; Le Viet Duc, L Duc; Phung, Dinh.

In: Pervasive and Mobile Computing, Vol. 38, 07.2017, p. 396-417.

Research output: Contribution to journalArticleAcademicpeer-review

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