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.
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Aggarwal CC, Han J, Wang J, Yu PS (2003) framework for clustering evolving data streams. In: Proceedings of the 29th VLDB conference
Associated Press (2005) Tracking cell phones for real-time traffic data
Babcock B, Babu S, Datar M, Motwanim R, Widom J (2002) Models and issues in data stream systems. In: Proceedings of the 21st ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems, pp 1–16
Belardo S, Karwan KR, Wallace WA (1984) Managing the response to disasters using microcomputers. Interfaces 14(2):29–39
Bentley JL (1975) Multidimensional binary search trees used for associative searching. Commun ACM 18(9):509–517
Bicking C, Gryna Jr FM (1979) Quality control handbook. McGraw-Hill, New York
Cheu EY, Keongg C, Zhou Z (2004) On the two-level hybrid clustering algorithm. In: International conference on artificial intelligence in science and technology, pp 138–142
Chipman H, Tibshirani R (2006) Hybrid hierarchical clustering with applications to microarray data. Biostatistics 7(2):286–301
Guha S, Meyerson A, Mishra N, Motwani R, O’Callaghan L (2003) Clustering data streams: theory and practice. IEEE Trans Knowl Data Eng 3:515–528
Hartigan JA (1975) Clustering algorithms. Wiley series in probability and mathematical statistics. Wiley, New York
Hodge VJ, Austin J (2004) A survey of outlier detection methodologies. Artif Intell Rev 22:85–126
Jain AK, Dubes RC (1998) Algorithms for clustering data. Prentice-Hall, Englewood Cliffs
Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surveys 31(3):264–323
Jennex ME (2007) Modeling emergency response systems. In: Proceedings of the 40th Hawaii international conference on system sciences
Madey GR, Barabási AL, Chawla NV, Gonzalez M, Hachen D, Lantz B, Pawling A, Schoenharl T, Szabó G, Wang P, Yan P (2007) Enhanced situational awareness: application of DDDAS concepts to emergency and disaster management. In: Alexandrov VN, van Albada GD, Sloot PMA, Dongarra J (eds) International conference on computational science. Lecture notes in computer science. Springer, Berlin
Markou M, Singh S (2003a) Novelty detection: a review. part 1: statistical approaches. Signal Process 83(12):2481–2497
Markou M, Singh S (2003b) Novelty detection: a review. part 2: neural network based approaches. Signal Process 83(12):2499–2521
Moore A (1999) Very fast EM-based mixture model clustering using multiresolution kd-trees. In: Kearns M, Cohn D (eds) Advances in neural information processing systems. Kaufman, Los Altos, pp 543–549
National Science Foundation (2006) Real-time traffic routing from the comfort of your car. Press release 06-124. http://www.nsf.gov/news/news_summ.jsp?cntn_id=107972
Pelleg D, Moore A (1999) Accelerating exact k-means algorithms with geometric reasoning. In: Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 277–281
Portnoy L, Eskin E, Stolfo S (2001) Intrusion detection with unlabeled data using clustering. In: ACM workshop on data mining applied to security
Schoenharl T, Bravo R, Madey G (2006a) WIPER: leveraging the cell phone network for emergency response. Int J Intell Control Syst 11(4):209–216
Schoenharl T, Madey G, Szabó G, Barabási AL (2006b) WIPER: multi-agent system for emergency response. In: Proceedings of the 3rd international ISCRAM conference
Sillem S, Wiersma E (2006) Comparing cell broadcast and text messaging for citizen warning. In: Proceedings of the 3rd international ISCRAM conference
Smart C, Vertinsky I (1977) Designs for crisis decision units. Adm Sci Q 22:640–657
Surdeanu M, Turmo J, Ageno A (2005) A hybrid unsupervised approach for document clustering. In: Proceedings of the 5th ACM SIGKDD international conference on knowledge discovery and data mining
Tsymbal A (2004) The problem of concept drift: definitions and related work. Technical report TCD-CS-2004-15, Trinity College, Dublin
Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. Kaufman, Los Altos
Wood M (2005) Cell@lert, for government-to-citizen mass communications in emergencies; it’s about time. In: Proceedings of the second international ISCRAM conference
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This paper won the best student paper award at the North American Association for Computational and Organizational Science (NAACSOS) Conference 2006, University of Notre Dame, Notre Dame, IN, USA.
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Pawling, A., Chawla, N.V. & Madey, G. Anomaly detection in a mobile communication network. Comput Math Organiz Theor 13, 407–422 (2007). https://doi.org/10.1007/s10588-007-9018-7
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DOI: https://doi.org/10.1007/s10588-007-9018-7