Attribute Outlier Detection over Data Streams

  • Hui Cao
  • Yongluan Zhou
  • Lidan Shou
  • Gang Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5982)


Outlier detection is widely used in many data stream application, such as network intrusion detection, fraud detection, etc. However, most existing algorithms focused on detecting class outliers and there is little work on detecting attribute outliers, which considers the correlation or relevance among the data items. In this paper we study the problem of detecting attribute outliers within the sliding windows over data streams. An efficient algorithm is proposed to perform exact outlier detection. The algorithm relies on an efficient data structure, which stores only the necessary information and can perform updates incurred by data arrival and expiration with minimum cost. To address the problem of limited memory, we also present an approximate algorithm, which selectively drops data within the current window and at the same time maintains a maximum error bound. Extensive experiments are conducted and the results show that our algorithms are efficient and effective.


attribute outlier date stream 


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  1. 1.
    Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for clustering evolving data streams. In: VLDB 2003: Proceedings of the 29th international conference on Very large data bases, pp. 81–92. VLDB Endowment (2003)Google Scholar
  2. 2.
    Angiulli, F., Fassetti, F.: Detecting distance-based outliers in streams of data. In: CIKM 2007: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, pp. 811–820. ACM, New York (2007)CrossRefGoogle Scholar
  3. 3.
    Barnett, V., Lewis, T.: Outliers in statistical data (1984)Google Scholar
  4. 4.
    Breunig, M.M., Kriegel, H.-P., Ng, R.T., Sander, J.: Lof: identifying density-based local outliers. SIGMOD Rec. 29(2), 93–104 (2000)CrossRefGoogle Scholar
  5. 5.
    Cao, H., Zhou, Y., Shou, L., Chen, G.: Attribute outlier detection over data streams, 9 (2009),
  6. 6.
    Hawkins, D.: Identification of outliers. Chapman and Hall, Reading (1980)zbMATHGoogle Scholar
  7. 7.
    Guha, S., Meyerson, A., Mishra, N., Motwani, R., O’Callaghan, L.: Clustering data streams: Theory and practice. IEEE Trans. Knowl. Data Eng. 15(3), 515–528 (2003)CrossRefGoogle Scholar
  8. 8.
    Jiang, M.-F., Tseng, S.-S., Su, C.-M.: Two-phase clustering process for outliers detection. Pattern Recognition Letters 22(6/7), 691–700 (2001)zbMATHCrossRefGoogle Scholar
  9. 9.
    Knorr, E.M., Ng, R.T.: A unified notion of outliers: Properties and computation. In: KDD, pp. 219–222 (1997)Google Scholar
  10. 10.
    Knorr, E.M., Ng, R.T.: Algorithms for mining distance-based outliers in large datasets. In: VLDB 1998: Proceedings of the 24th International Conference on Very Large Data Bases, pp. 392–403. Morgan Kaufmann Publishers Inc., San Francisco (1998)Google Scholar
  11. 11.
    Koh, J.L.Y., Lee, M.-L., Hsu, W., Ang, W.T.: Correlation-based attribute outlier detection in XML. In: ICDE 2008: Proceedings of the 24th International Conference on Data Engineering, pp. 1522–1524 (2008)Google Scholar
  12. 12.
    Koh, J.L.Y., Lee, M.-L., Hsu, W., Lam, K.-T.: Correlation-based detection of attribute outliers. In: Kotagiri, R., Radha Krishna, P., Mohania, M., Nantajeewarawat, E. (eds.) DASFAA 2007. LNCS, vol. 4443, pp. 164–175. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Zhang, J., Gao, Q., Wang, H.: Spot: A system for detecting projected outliers from high-dimensional data streams. In: ICDE 2008: Proceedings of the 24th International Conference on Data Engineering, pp. 1628–1631. IEEE, Los Alamitos (2008)CrossRefGoogle Scholar
  14. 14.
    Zhang, T., Ramakrishnan, R., Livny, M.: Birch: an efficient data clustering method for very large databases. In: SIGMOD 1996: Proceedings of the 1996 ACM SIGMOD international conference on Management of data, pp. 103–114. ACM, New York (1996)CrossRefGoogle Scholar
  15. 15.
    Zhou, A., Cao, F., Qian, W., Jin, C.: Tracking clusters in evolving data streams over sliding windows. Knowl. Inf. Syst. 15(2), 181–214 (2008)CrossRefGoogle Scholar
  16. 16.
    Zhu, X., Wu, X.: Class noise vs. attribute noise: A quantitative study. Artif. Intell. Rev. 22(3), 177–210 (2004)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Hui Cao
    • 1
  • Yongluan Zhou
    • 2
  • Lidan Shou
    • 1
  • Gang Chen
    • 1
  1. 1.College of Computer ScienceZhejiang UniversityChina
  2. 2.Department of Mathematics and Computer scienceUniversity of Southern DenmarkDenmark

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