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A probabilistic approach to mining mobile phone data sequences

A probabilistic approach to mining mobile phone data sequences
A probabilistic approach to mining mobile phone data sequences
We present a new approach to address the problem of large sequence mining from big data. The particular problem of interest is the effective mining of long sequences from large-scale location data to be practical for Reality Mining applications, which suffer from large amounts of noise and lack of ground truth. To address this complex data, we propose an unsupervised probabilistic topic model called the distant n-gram topic model (DNTM). The DNTM is based on latent Dirichlet allocation (LDA), which is extended to integrate sequential information. We define the generative process for the model, derive the inference procedure, and evaluate our model on both synthetic data and real mobile phone data. We consider two different mobile phone datasets containing natural human mobility patterns obtained by location sensing, the first considering GPS/wi-fi locations and the second considering cell tower connections. The DNTM discovers meaningful topics on the synthetic data as well as the two mobile phone datasets. Finally, the DNTM is compared to LDA by considering log-likelihood performance on unseen data, showing the predictive power of the model. The results show that the DNTM consistently outperforms LDA as the sequence length increases.
1617-4909
223-238
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Gatica-Perez, Daniel
8eaa6830-e159-470f-85e1-fb88ea8846fa
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Gatica-Perez, Daniel
8eaa6830-e159-470f-85e1-fb88ea8846fa

Farrahi, Katayoun and Gatica-Perez, Daniel (2014) A probabilistic approach to mining mobile phone data sequences. Personal and Ubiquitous Computing, 18 (1), 223-238. (doi:10.1007/s00779-013-0640-8).

Record type: Article

Abstract

We present a new approach to address the problem of large sequence mining from big data. The particular problem of interest is the effective mining of long sequences from large-scale location data to be practical for Reality Mining applications, which suffer from large amounts of noise and lack of ground truth. To address this complex data, we propose an unsupervised probabilistic topic model called the distant n-gram topic model (DNTM). The DNTM is based on latent Dirichlet allocation (LDA), which is extended to integrate sequential information. We define the generative process for the model, derive the inference procedure, and evaluate our model on both synthetic data and real mobile phone data. We consider two different mobile phone datasets containing natural human mobility patterns obtained by location sensing, the first considering GPS/wi-fi locations and the second considering cell tower connections. The DNTM discovers meaningful topics on the synthetic data as well as the two mobile phone datasets. Finally, the DNTM is compared to LDA by considering log-likelihood performance on unseen data, showing the predictive power of the model. The results show that the DNTM consistently outperforms LDA as the sequence length increases.

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e-pub ahead of print date: 20 February 2013
Published date: 2014

Identifiers

Local EPrints ID: 419446
URI: http://eprints.soton.ac.uk/id/eprint/419446
ISSN: 1617-4909
PURE UUID: 0ffd27d5-185f-430d-b4b8-bf4674d96000

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Date deposited: 12 Apr 2018 16:30
Last modified: 16 Mar 2020 17:30

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