Identifying Anomalous Social Contexts from Mobile Proximity Data Using Binomial Mixture Models

  • Eric Malmi
  • Juha Raitio
  • Oskar Kohonen
  • Krista Lagus
  • Timo Honkela
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7619)


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.


anomaly detection social context mobile proximity data mixture models 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Eric Malmi
    • 1
  • Juha Raitio
    • 1
  • Oskar Kohonen
    • 1
  • Krista Lagus
    • 1
  • Timo Honkela
    • 1
  1. 1.Department of Information and Computer ScienceAalto UniversityAaltoFinland

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