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Anomaly detection in a mobile communication network

Author

Listed:
  • Alec Pawling

    (University of Notre Dame)

  • Nitesh V. Chawla

    (University of Notre Dame)

  • Greg Madey

    (University of Notre Dame)

Abstract

Mobile communication networks produce massive amounts of data which may be useful in identifying the location of an emergency situation and the area it affects. We propose a one pass clustering algorithm for quickly identifying anomalous data points. We evaluate this algorithm’s ability to detect outliers in a data set and describe how such an algorithm may be used as a component of an emergency response management system.

Suggested Citation

  • Alec Pawling & Nitesh V. Chawla & Greg Madey, 2007. "Anomaly detection in a mobile communication network," Computational and Mathematical Organization Theory, Springer, vol. 13(4), pages 407-422, December.
    • Handle: RePEc:spr:comaot:v:13:y:2007:i:4:d:10.1007_s10588-007-9018-7
    DOI: 10.1007/s10588-007-9018-7
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    References listed on IDEAS

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    1. Salvatore Belardo & Kirk R. Karwan & William A. Wallace, 1984. "Managing the Response to Disasters Using Microcomputers," Interfaces, INFORMS, vol. 14(2), pages 29-39, April.
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    Cited by:

    1. Steenbruggen, John & Tranos, Emmanouil & Nijkamp, Peter, 2015. "Data from mobile phone operators: A tool for smarter cities?," Telecommunications Policy, Elsevier, vol. 39(3), pages 335-346.
    2. Sébastien Dujardin & Damien Jacques & Jessica Steele & Catherine Linard, 2020. "Mobile Phone Data for Urban Climate Change Adaptation: Reviewing Applications, Opportunities and Key Challenges," Sustainability, MDPI, vol. 12(4), pages 1-17, February.
    3. Xin Lu & David J. Wrathall & Pål Roe Sundsøy & Md. Nadiruzzaman & Erik Wetter & Asif Iqbal & Taimur Qureshi & Andrew J. Tatem & Geoffrey S. Canright & Kenth Engø-Monsen & Linus Bengtsson, 2016. "Detecting climate adaptation with mobile network data in Bangladesh: anomalies in communication, mobility and consumption patterns during cyclone Mahasen," Climatic Change, Springer, vol. 138(3), pages 505-519, October.
    4. Markus Goldstein & Seiichi Uchida, 2016. "A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-31, April.

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