Reference:

Eric Malmi, Juha Raitio, Oskar Kohonen, Krista Lagus, and Timo Honkela. Identifying anomalous social contexts from mobile proximity data using binomial mixture models. In Jaakko Hollmén, Frank Klawonn, and Allan Tucker, editors, Advances in Intelligent Data Analysis XI, pages 195–206, 2012.

Abstract:

Mobile proximity information provides a rich and detailed view into the social interactions of mobile phone users, allowing novel empirical studies of human behavior and context-aware applications. In this study, we apply a statistical anomaly detection method based on multivariate binomial mixture models to mobile proximity data from 106 users. The method detects days when a person's social context is unexpected, and it provides a clustering of days based on the contexts. We present a detailed analysis regarding one user, identifying days with anomalous contexts, and potential reasons for the anomalies. We also study the overall anomalousness of people's social contexts. This analysis reveals a clear weekly oscillation in the predictability of the contexts and a weekend-like behavior on public holidays.

Suggested BibTeX entry:

@inproceedings{SocialContexts12,
    author = {Eric Malmi and Juha Raitio and Oskar Kohonen and Krista Lagus and Timo Honkela},
    booktitle = {Advances in Intelligent Data Analysis XI},
    editor = {Jaakko Hollm{\'e}n and Frank Klawonn and Allan Tucker},
    language = {eng},
    pages = {195-206},
    title = {Identifying Anomalous Social Contexts from Mobile Proximity Data Using Binomial Mixture Models},
    year = {2012},
}