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)

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.

Keywords

anomaly detection social context mobile proximity data mixture models 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Eagle, N., Pentland, A.: Reality mining: sensing complex social systems. Personal and Ubiquitous Computing 10(4), 255–268 (2006)CrossRefGoogle Scholar
  2. 2.
    Snipen, L., Almøy, T., Ussery, D.: Microbial comparative pan-genomics using binomial mixture models. BMC Genomics 10(1), 385 (2009)CrossRefGoogle Scholar
  3. 3.
    Vatanen, T., Kuusela, M., Malmi, E., Raiko, T., Aaltonen, T., Nagai, Y.: Semi-supervised detection of collective anomalies with an application in high energy particle physics. In: Proc. IJCNN 2012, Brisbane, Australia (to appear, 2012)Google Scholar
  4. 4.
    Yamanishi, K., Takeuchi, J.I., Williams, G., Milne, P.: On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms. Data Mining and Knowledge Discovery 8(3), 275–300 (2004)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Roberts, S., Tarassenko, L.: A probabilistic resource allocating network for novelty detection. Neural Computation 6(2), 270–284 (1994)CrossRefGoogle Scholar
  6. 6.
    Zhang, A., Ye, W., Wen, M.: Detection of anomaly collective call patterns. Journal of Computational Information Systems 7(10), 3614–3622 (2011)Google Scholar
  7. 7.
    Do, T.M.T., Gatica-Perez, D.: Human interaction discovery in smartphone proximity networks. Personal and Ubiquitous Computing, 1–19Google Scholar
  8. 8.
    Kiukkonen, N., Blom, J., Dousse, O., Gatica-Perez, D., Laurila, J.: Towards rich mobile phone datasets: Lausanne data collection campaign. In: Proc. ICPS, Berlin (2010)Google Scholar
  9. 9.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: A survey. ACM Computing Surveys (CSUR) 41(3), 15 (2009)CrossRefGoogle Scholar
  10. 10.
    Getzfeld, A.R.: Essentials of abnormal psychology, vol. 5. Wiley (2006)Google Scholar
  11. 11.
    McLachlan, G.J., Peel, D.: Finite mixture models, vol. 299. Wiley- Interscience (2000)Google Scholar
  12. 12.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 1–38 (1977)Google Scholar
  13. 13.
    Banerjee, A., Merugu, S., Dhillon, I.S., Ghosh, J.: Clustering with Bregman divergences. The Journal of Machine Learning Research 6, 1705–1749 (2005)MathSciNetMATHGoogle Scholar
  14. 14.
    Stone, M.: Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society. Series B (Methodological), 111–147 (1974)Google Scholar
  15. 15.
    Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real- time tracking. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 246–252 (1999)Google Scholar

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

Personalised recommendations