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.
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Malmi, E., Raitio, J., Kohonen, O., Lagus, K., Honkela, T. (2012). Identifying Anomalous Social Contexts from Mobile Proximity Data Using Binomial Mixture Models. In: Hollmén, J., Klawonn, F., Tucker, A. (eds) Advances in Intelligent Data Analysis XI. IDA 2012. Lecture Notes in Computer Science, vol 7619. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34156-4_19
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DOI: https://doi.org/10.1007/978-3-642-34156-4_19
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