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RODD: An Effective Reference-Based Outlier Detection Technique for Large Datasets

  • Conference paper
Advanced Computing (CCSIT 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 133))

Abstract

Outlier detection has gained considerable interest in several fields of research including various sciences, medical diagnosis, fraud detection, and network intrusion detection. Most existing techniques are either distance based or density based. In this paper, we present an effective reference point based outlier detection technique (RODD) which performs satisfactorily in high dimensional real-world datasets. The technique was evaluated in terms of detection rate and false positive rate over several synthetic and real-world datasets and the performance is excellent.

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References

  1. Knorr, E.M., Ng, R.T.: Algorithms for mining distance-based outliers in large datasets. In: Proc of VLDB 1998, USA, pp. 392–403. Morgan Kaufmann, San Francisco (1998)

    Google Scholar 

  2. Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Proc of ACM SIGMOD on Management of Data, Washington, D.C., pp. 207–216. ACM Press, New York (1993)

    Google Scholar 

  3. Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Wadsworth and Brooks, Monterey (1984)

    MATH  Google Scholar 

  4. Ester, M., Kriegel, H.-p., Jörg, S., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proc of KDD, pp. 226–231. AAAI Press, Menlo Park (1996)

    Google Scholar 

  5. Hawkins, D.M.: Indentification of outliers. Chapman and Hall, London (1980)

    Book  MATH  Google Scholar 

  6. Rousseeuw, P., Leroy, A.: Robust Regression and Outlier Detection, 3rd edn. John Wiley & Sons, Chichester (1996)

    MATH  Google Scholar 

  7. Tan, P., Steinbach, M., Kumar, V.: Introduction to data mining. Pearson Addison Wesley, London (2005)

    Google Scholar 

  8. Hodge, V., Austin, J.: A survey of outlier detection methodologies. Artificial Intelligence Review 22, 85–126 (2004)

    Article  MATH  Google Scholar 

  9. Wang, B., Wang, G.-R., Yu, G.: Outlier detection over sliding windows for probabilistic data streams. Journal of Computer Science and Technology 25(3), 389–400 (2010)

    Article  Google Scholar 

  10. Knorr, E.M., Ng, R.T., Tucakov, V.: Distance-based outliers: Algorithms and applications. VLDB Journal 8, 237–253 (2000)

    Article  Google Scholar 

  11. Sequeira, K., Zaki, M.: Admit: Anomaly-based data mining for intrusions. In: ACM SIGKDD, pp. 386–395 (2002)

    Google Scholar 

  12. Angiulli, F., Basta, S., Pizzuti, C.: Distance-based detection and prediction of outliers. IEEE TKDE 18, 145–160 (2006)

    MATH  Google Scholar 

  13. Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: Lof: Identifying density-based local outliers. In: ACM SIGMOD on Management of Data, pp. 386–395 (2000)

    Google Scholar 

  14. Papadimitriou, S., Kitawaga, H., Gibbons, P.B., Faloutsos, C.: Loci: Fast outlier detection using the local correlation integral. In: ICDE (2003)

    Google Scholar 

  15. Agrawal, A.: Local subspace based outlier detection. In: Communication in Computer and Information Science, vol. 40, pp. 149–157. Springer, Heidelberg (2009)

    Google Scholar 

  16. Varun, C., Arindam, B., Vipin, K.: Outlier detection - a survey. Technical report, Dept of CSE,University of Minnesota, USA (2007)

    Google Scholar 

  17. Markou, M., Singh, S.: Novelty detection: a review-part 1: statistical approaches. Signal Processing 83, 2481–2497 (2003)

    Article  MATH  Google Scholar 

  18. Pei, Y., Zaiane, O.R., Gao, Y.: An efficient reference-based approach to outlier detection in large datasets. In: Proc of ICDM 2006, USA, pp. 478–487. IEEE, Los Alamitos (2006)

    Google Scholar 

  19. Portnoy, L., Eskin, E., Stolfo, S.: Intrusion detection with unlabeled data using clustering. In: Proc of ACM CSS Workshop on DMAS, Philadelphia, PA, pp. 5–8 (2001)

    Google Scholar 

  20. Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficient k-means clustering algorithm: Analysis and implementation. IEEE TPAMI 24(7), 881–892 (2002)

    Article  MATH  Google Scholar 

  21. Lohninger, H.: Teach/Me Data Analysis. Springer, Heidelberg (1999)

    MATH  Google Scholar 

  22. Barczynski, R.: System outlier mining (2010), http://sites.google.com/site/rafalba/

  23. Bay, S., Schwabacher, M.: Mining distance-based outliers in near linear time with randomization and a simple pruning rule. In: Proc of the Ninth ACM SIGKDD, pp. 29–38 (2003)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Dept. of Computer Science & Engineering, Tezpur University, Napaam, India

    Monowar H. Bhuyan & D. K. Bhattacharyya

  2. Dept. of Computer Science, University of Colorado, CO, 80918, USA

    J. K. Kalita

Authors
  1. Monowar H. Bhuyan
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  2. D. K. Bhattacharyya
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  3. J. K. Kalita
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Editor information

Editors and Affiliations

  1. Jackson State University, 39217, Jackson, MS, USA

    Natarajan Meghanathan

  2. Deptt. of Electronics and Computer Engg, Indian Institute of Technology, Roorkee, India

    Brajesh Kumar Kaushik

  3. Wireilla Net Solutions PTY Ltd, Melbourne, Victoria, Australia

    Dhinaharan Nagamalai

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© 2011 Springer-Verlag Berlin Heidelberg

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Bhuyan, M.H., Bhattacharyya, D.K., Kalita, J.K. (2011). RODD: An Effective Reference-Based Outlier Detection Technique for Large Datasets. In: Meghanathan, N., Kaushik, B.K., Nagamalai, D. (eds) Advanced Computing. CCSIT 2011. Communications in Computer and Information Science, vol 133. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17881-8_8

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  • DOI: https://doi.org/10.1007/978-3-642-17881-8_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17880-1

  • Online ISBN: 978-3-642-17881-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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