SpringerLink
Log in

Enhancing Effectiveness of Outlier Detections for Low Density Patterns

  • Conference paper
  • First Online:
Advances in Knowledge Discovery and Data Mining (PAKDD 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2336))

Included in the following conference series:

Abstract

Outlier detection is concerned with discovering exceptional behaviors of objects in data sets. It is becoming a growingly useful tool in applications such as credit card fraud detection, discovering criminal behaviors in e-commerce, identifying computer intrusion, detecting health problems, etc. In this paper, we introduce a connectivity-based outlier factor (COF) scheme that improves the effectiveness of an existing local outlier factor (LOF) scheme when a pattern itself has similar neighbourhood density as an outlier. We give theoretical and empirical analysis to demonstrate the improvement in effectiveness and the capability of the COF scheme in comparison with the LOF scheme.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 85.59
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 106.99
Price includes VAT (Germany)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. A. Arning, R. Agrawal, P. Raghavan: ”A Linear Method for Deviation detection in Large Databases”, Proc. of 2nd Intl. Conf. On Knowledge Discovery and Data Mining, 1996, pp 164–169.

    Google Scholar 

  2. V. Barnett, T. Lewis: ”Outliers in Statistical Data”, John Wiley, 1994.

    Google Scholar 

  3. M. Breuning, Hans-Peter Kriegel, R. Ng, J. Sander: ”LOF: Identifying density based Local Outliers”, Proc. of the ACM SIGMOD Conf. On Management of Data, 2000.

    Google Scholar 

  4. W. DuMouchel, M. Schonlau: ”A Fast Computer Intrusion Detection Algorithm based on Hypothesis Testing of Command Transition Probabilities”, Proc.of 4th Intl. Conf. On Knowledge Discovery and Data Mining, 1998, pp. 189–193.

    Google Scholar 

  5. M. Ester, H. Kriegel, J. Sander, X. Xu: ”A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise”, Proc. of 2nd Intl. Conf. On Knowledge Discovery and Data Mining, 1996, pp 226–231.

    Google Scholar 

  6. T. Fawcett, F. Provost: ”Adaptive Fraud Detection”, Data Mining and Knowledge Discovery Journal, Kluwer Academic Publishers, Vol. 1, No. 3, 1997, pp 291–316.

    Article  Google Scholar 

  7. D. Hawkins: ”Identification of Outliers”, Chapman and Hall, London, 1980.

    MATH  Google Scholar 

  8. E. Knorr, R. Ng: ”Algorithms for Mining Distance based Outliers in Large Datasets”, Proc. of 24th Intl. Conf. On Very Large Data Bases, 1998, pp 392–403.

    Google Scholar 

  9. E. Knorr, R. Ng: ”Finding Intensional Knowledge of Distance-based Outliers”, Proc. of 25th Intl. Conf. On Very Large Data Bases, 1999, pp 211–222.

    Google Scholar 

  10. R. Ng, J. Han: ”Efficient and Effective Clustering Methods for Spatial Data Mining”, Proc. of 20th Intl. Conf. On Very Large Data Bases, 1994, pp 144–155.

    Google Scholar 

  11. S. Ramaswamy, R. Rastogi, S. Kyuseok: ”Efficient Algorithms for Mining Outliers from Large Data Sets”, Proc. of ACM SIGMOD Intl. Conf. On Management of Data, 2000, pp 427–438.

    Google Scholar 

  12. N. Roussopoulos, S. Kelley, F. Vincent, ”Nearest Neighbor Queries”, Proc. of ACM SIGMOD Intl. Conf. On Management of Data, 1995, pp 71–79.

    Google Scholar 

  13. G. Sheikholeslami, S. Chatterjee, A. Zhang: ”WaveCluster: A multi-Resolution Clustering Approach for Very Large Spatial Databases”, Proc. of 24th Intl. Conf. On Very Large Data Bases, 1998, pp 428–439.

    Google Scholar 

  14. S. Guha, R. Rastogi, K. Shim: ”Cure: An Efficient Clustering Algorithm for Large Databases”, In Proc. of the ACM SIGMOD Conf. On Management of Data, 1998, pp 73–84.

    Google Scholar 

  15. J. Tang, Z. Chen, A. Fu and D. Cheung: ”A General Framework for Outlier Formulations: Density versus Connectivity”, Manuscript.

    Google Scholar 

  16. T. Zhang, R. Ramakrishnan, M. Linvy: ”BIRCH: An Efficient Data Clustering Method for Very Large Databases”, Proc. of ACM SIGMOD Intl. Conf. On Management of Data, 1996, pp 103–114.

    Google Scholar 

Download references

Author information

Author notes

  1. Jian Tang

    Present address: Memorial University of Newfoundland, Canada

Authors and Affiliations

  1. Department of Computer Science and Engineering, Chinese University of Hong Kong, Shatin, Hong Kong

    Jian Tang & Ada Wai-chee Fu

  2. Department of Computer Science, University of Texas at Pan-America, Texas, USA

    Zhixiang Chen

  3. Department of Computer Science and Information Systems, University of Hong Kong, Pokfulam, Hong Kong

    David W. Cheung

Authors
  1. Jian Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhixiang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ada Wai-chee Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. David W. Cheung
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. EE Department, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, Taiwan, ROC

    Ming-Syan Chen

  2. IBM Thomas J. Watson Research Center, 30 Sawmill River Road, Hawthorne, NY, 10532, USA

    Philip S. Yu

  3. School of Computing, National University of Singapore, Lower Kent Ridge Road, Singapore, 119260

    Bing Liu

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Tang, J., Chen, Z., Fu, A.Wc., Cheung, D.W. (2002). Enhancing Effectiveness of Outlier Detections for Low Density Patterns. In: Chen, MS., Yu, P.S., Liu, B. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2002. Lecture Notes in Computer Science(), vol 2336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47887-6_53

Download citation

  • DOI: https://doi.org/10.1007/3-540-47887-6_53

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43704-8

  • Online ISBN: 978-3-540-47887-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation