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Artificial Intelligence Review

, Volume 22, Issue 2, pp 85–126 | Cite as

A Survey of Outlier Detection Methodologies

  • Victoria J. Hodge
  • Jim Austin
Article

Abstract

Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review.

Keywords

anomaly detection deviation noise novelty outlier recognition 

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References

  1. Aggarwal, C. C. Yu, P. S. (2001). Outlier Detection for High Dimensional Data. Proceedings of the ACM SIGMOD Conference 2001.Google Scholar
  2. Aha, D. W. Bankert, R. B. (1994). Feature Selection for Case-Based Classification of Cloud Types: An Empirical Comparison. Proceedings of the AAAI-94 Workshop on Case-Based Reasoning.Google Scholar
  3. Allan J, Carbonell J, Doddington G, Yamron J, Yang Y (1998) Topic Detection and Tracking Pilot Study: Final Report. Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop.Google Scholar
  4. Arning, A., Agrawal, R., Raghavan, P. 1996A Linear Method for Deviation Detection in Large DatabasesProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining164169Google Scholar
  5. Baker, L. D., Hofmann, T., McCallum, A. K. Yang, Y. (1999). A Hierarchical Probabilistic Model for Novelty Detection in Text. NIPS’99, Unpublished manuscript.Google Scholar
  6. Barnett, V., Lewis, T. 1994Outliers in Statistical Data 3rd ednJohn Wiley SonsNYGoogle Scholar
  7. Beale, R., Jackson, T. 1990Neural Computing: An IntroductionInstitute of Physics PublishingBristol, UK and Philadelphia, PAGoogle Scholar
  8. Bishop, C.M. 1994Novelty detection Neural Network validationProceedings of the IEE Conference on Vision, Image and Signal Processing217222Google Scholar
  9. Bishop, C.M. 1995Neural Networks for Pattern RecognitionOxford University PressOxfordGoogle Scholar
  10. Blake, C. L. Merz, C. J. (1998). UCI Repository of Machine Learning Databases, http://www.ics.uci.edu/mlearn/MLRepository.html, University of California, Irvine, Department of Information and Computer Sciences.Google Scholar
  11. Bolton, R. J., Hand, D. J. 2001Unsupervised Profiling Methods for Fraud Detection. Credit Scoring and Credit Control VIIEdinburgh57Google Scholar
  12. Bradley, P.S., FayyadUM Mangasarian, O.L., Mangasarian, U.M. 1999Mathematical Programming for Data Mining: Formulations and Challenges. INFORMSJournal on Computing11217238Google Scholar
  13. Breiman, L., Friedman, J., Olshen, R., Stone, C. 1984Classification and Regression TreesWadsworth International Group.Belmont CAGoogle Scholar
  14. Brodley, C. E., Friedl, M. A. 1996Identifying and Eliminating Mislabeled Training Instances.Proceedings of the 13th National Conference on Artificial Intelligence,AAAI Press799805Google Scholar
  15. Brotherton, T., Johnson, T., Chadderdon, G. 1998Classification and Novelty Detection using Linear Models and a Class Dependent – Elliptical Bassi Function Neural Network Proceedings of the International conference on neural networksAnchorageAlaskaGoogle Scholar
  16. Byers, S., Raftery, A.E. 1998Nearest Neighbor Clutter Removal for Estimating Features in Spatial Point ProcessesJournal of the American Statistical Association93577584Google Scholar
  17. Carpenter, G., Grossberg, S. 1987‘A Massively Parallel Architecture for a Self-Organizing Neural Pattern Recognition Machine’Computer Vision, Graphics, and Image Processing3754115Google Scholar
  18. Caudell, T P, Newman, DS 1993An Adaptive Resonance Architecture to Define Normality and Detect Novelties in Time Series and Databases.IEEE World Congress on Neural NetworksOregonPortland166176Google Scholar
  19. Cohen, W. W. (1995). Fast Effective Rule Induction. International Conference on Machine Learning, 115–123.Google Scholar
  20. Crook P, Hayes G (1995) A Robot Implementation of a Biologically Inspired Method for Novelty Detection. Proceedings of TIMR-2001, Towards Intelligent Mobile Robots Manchester.Google Scholar
  21. Dasgupta, D., Forrest, S. 1996Novelty Detection in Time Series Data Using Ideas from Immunology.Proceedings of the Fifth International Conference on Intelligent Systems.Google Scholar
  22. Datta, P., Kibler, D. 1995Learning prototypical concept descriptionsProceedings of the 12th International Conference on Machine LearningMorgan Kaufmann158166Google Scholar
  23. DeCoste, D. Levine, M. B. (2000). Automated Event Detection in Space Instruments: A Case Study Using IPEX-2 Data and Support Vector Machines. Proceedings of the SPIE Conference on Astronomical Telescopes and Space Instrumentation.Google Scholar
  24. Dietterich, T. G., Michalski, R. S. 1986Learning to Predict Sequences.Michalski, Carbonell, Mitchell,  eds. Machine Learning: An Artificial Intelligence ApproachSan Mateo, CAMorgan KaufmannGoogle Scholar
  25. Ester, M., Kriegel, H. -P., Xu, X. 1996A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with NoiseProceedings of the Second International Conference on Knowledge Discovery and Data MiningAAAI PressPortland, Oregon226231Google Scholar
  26. Faloutsos, C., Korn, F., Labrinidis, A., Kotidis, Y., Kaplunovich, A. Perkovic, D. (1997). Quantifiable Data Mining Using Principal Component Analysis. Technical Report CS-TR-3754, Institute for Systems Research, University of Maryland, College Park, MD.Google Scholar
  27. Fawcett, T., Provost, F.J. 1999Activity Monitoring: Noticing Interesting Changes in BehaviorProceedings of the h ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 125362Google Scholar
  28. Francis, J., Addison, D., Wermter, S. MacIntyre, J. (1999). Effectiveness of Feature Extraction in Neural Network Architectures for Novelty Detection. Proceedings of the ICANN Conference.Google Scholar
  29. Grubbs, F.E. 1969Procedures for Detecting Outlying Observations in SamplesTechnometrics11121Google Scholar
  30. Hickinbotham, S., Austin, J. 2000Novelty Detection in Airframe Strain DataProceedings of the International Conference on Pattern Recognition Barcelona 1215536539Google Scholar
  31. Himberg J, Jussi, A., Alhoniemi, E., Vesanto, J. Simula, O. (2001). The Self-Organizing Map as a Tool in Knowledge Engineering, In Pattern Recognition in Soft Computing Paradigm, 38–65. Soft Computing. World Scientific Publishing.Google Scholar
  32. Hollier, G., Austin, J. 2002Novelty Detection for Strain-Gauge Degradation Using Maximally Correlated Components.Proceedings of the European Symposium on Artificial Neural NetworksESANN’2002Bruges257262Google Scholar
  33. Hollmen, J., Tresp, V. 1999Call-based Fraud Detection in Mobile Communication Networks using a Hierarchical Regime-Switching ModelAdvances in Neural Information Processing Systems – Proceedings of the 1998 Conference (NIPS’11)MIT Press889895Google Scholar
  34. Japkowicz, N., Myers, C., Gluck, M. A. 1995A Novelty Detection Approach to Classification.Proceedings of the 14th International Conference on Artificial Intelligence (IJCAI-95),518523Google Scholar
  35. John, G. H. 1995Robust Decision Trees: Removing Outliers from Databases.Proceedings of the First International Conference on Knowledge Discovery and Data MiningAAAI PressMenlo Park, CA174179Google Scholar
  36. Knorr, E. M., Ng, R. T. 1998Algorithms for Mining Distance-Based Outliers in Large Datasets.Proceedings of the VLDB ConferenceNew York, USA.392403Google Scholar
  37. Kohonen, T. 1997Self-Organizing MapsSpringer-VerlagHeidelbergVol 2Google Scholar
  38. Lane, T., Brodley, C. E. 1997aApplications of Machine Learning to Anomaly Detection.Adey, R.A.Rzevski, G.Teti, T. eds. Applications of Artificial Intelligence in Engineering X11,Southampton, UKComput. Mech. Publications.11314Google Scholar
  39. Lane, T., Brodley, C. E. 1997bSequence matching and learning in anomaly detection for computer securityAAAI Workshop: AI Approaches to Fraud Detection and Risk Management,AAAI Press4349Google Scholar
  40. Laurikkala, J., Juhola, M. Kentala, E. (2000). Informal Identification of Outliers in Medical Data. Fifth International Workshop on Intelligent Data Analysis in Medicine and Pharmacology IDAMAP-2000 Berlin, 22 August. Organized as a workshop of the 14th European Conference on Artificial Intelligence ECAI-2000.Google Scholar
  41. Marsland, S. (2001). On-Line Novelty Detection Through Self-Organisation, with Application to Inspection Robotics. Ph.D. Thesis, Faculty of Science and Engineering, University of Manchester, UK.Google Scholar
  42. Nairac, A., Townsend, N., Carr, R., King, S., CowleyP Tarassenko, L. 1999A System for the Analysis of Jet System Vibration DataIntegrated ComputerAided Engineering65365Google Scholar
  43. Ng, R. T. Han, J. (1994). Efficient and Effective Clustering Methods for Spatial Data Mining. Proceedings of the 20th International Conference on Very Large Data Bases, September 12–15, 1994, Santiago, Chile, 144–155. Morgan Kaufmann Publishers.Google Scholar
  44. Parra, L., Deco, G., Miesbach, S. 1996Statistical Independence and Novelty Detection with Information Preserving Nonlinear MapsNeural Computation8260269Google Scholar
  45. Prodromidis, A. L., Stolfo, S. J. 1998Mining Databases with Different Schemas: Integrating Incompatible Classifiers.Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining314318Google Scholar
  46. Quinlan, J.R. 1986Induction of Decision TreesMachine Learning181106Google Scholar
  47. Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann.Google Scholar
  48. Ramaswamy, S., Rastogi, R., Shim, K. 2000Efficient Algorithms for Mining Outliers from Large Data Sets.Proceedings of the ACM SIGMOD Conference on Management of DataDallas, TX427438Google Scholar
  49. Roberts, S.J. 1998Novelty Detection Using Extreme Value StatisticsIEE Proceedings on Vision, Image and Signal Processing146124129Google Scholar
  50. Roberts, S., Tarassenko, L. 1995A Probabilistic Resource Allocating Network for Novelty DetectionNeural. Computation6270284Google Scholar
  51. Rousseeuw, P., Leroy, A. 1996Robust Regression and Outlier Detection 3rd ednJohn Wiley & SonsNYGoogle Scholar
  52. Saunders, R., Gero, J.S. 2001aA Curious Design Agent: A Computational Model of Novelty-Seeking Behaviour in DesignProceedings of the Sixth Conference on Computer Aided Architectural Design Research in Asia (CAADRIA 2001)SydneyGoogle Scholar
  53. Saunders, R., Gero, J.S. 2001bDesigning for Interest and Novelty: Motivating Design AgentsProceedings of CAAD Futures 2001EindhovenGoogle Scholar
  54. Shekhar, S., Lu, C., Zhang, P. 2001Detecting Graph-Based Spatial Outliers: Algorithms and ApplicationsProceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Google Scholar
  55. Skalak, D. B. 1994Prototype and feature selection by sampling and random mutation hill climbing algorithmsMachine Learning: Proceedings of the Eleventh International Conference,293301Google Scholar
  56. Skalak, D. B., Rissland, E. L. 1990Inductive Learning in a Mixed Paradigm SettingProceedings of the Eighth National Conference on Artificial IntelligenceBoston, MA840847Google Scholar
  57. Smyth, P. 1994Markov Monitoring with Unknown StatesIEEE Journal on Selected Areas in Communications, Special Issue on Intelligent Signal Processing for Communications1216001612Google Scholar
  58. Stolfo, S. J., Prodromidis, A. L., Tselepis, S., Lee, W., Fan, D. W., Chan, P. K. 1997JAM: Java Agents for Meta-Learning over Distributed DatabasesProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining7481Google Scholar
  59. Tang, J., Chen, Z., Fu, A. Cheung, D. (2002). A Robust Outlier Detection Scheme in Large Data Sets, 6th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Taipei, Taiwan, May, 2002.Google Scholar
  60. Tax, D. M. J., Ypma, A., Duin, R. P. W. 1999Support Vector Data Description Applied to Machine Vibration Analysis.Proceedings of ASCI’99HeijenNetherlandsGoogle Scholar
  61. Taylor, O., Addison, D. 2000Novelty Detection Using Neural Network Technology.Proceedings of the COMADEN ConferenceGoogle Scholar
  62. Torr, P. H. S., Murray, D. W. 1993Outlier Detection and Motion SegmentationProceedings of SPIEGoogle Scholar
  63. Vesanto, J., Himberg, J., Siponen, M. Simula, O. (1998). Enhancing SOM Based Data Visualization. Proceedings of the 5th International Conference on Soft Computing and Information/Intelligent Systems. Methodologies for the Conception, Design and Application of Soft Computing, Vol. 1, 64–67, Singapore: World Scientific.Google Scholar
  64. Wettschereck, D. (1994). A Study of Distance-based Machine Learning Algorithms. Ph.D. Thesis, Department of Computer Science, Oregon State University, Corvallis.Google Scholar
  65. Ypma, A., Duin, R. P. 1997Novelty Detection Using Self-Organizing Maps.Kasabov, N.Kozma, R.Ko, K.O’Shea, R.Coghill, G.Gedeon, T. eds. Progress in Connectionist-Based Information Systems,SpringerLondon13221325vol. 2Google Scholar
  66. Zhang, T., Ramakrishnan, R. Livny, M. (1996). BIRCH: An Efficient Data Clustering Method for Very Large Databases’. In Jagadish, H. V. Mumick, I. S. (eds.) Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, Montreal, Quebec, Canada, June 4–6, 1996, 103–114. ACM Press.Google Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of YorkYorkUK

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