Skip to main content
Springer Nature Link
Log in

Robust maximum entropy clustering algorithm with its labeling for outliers

  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

In this paper, a novel robust maximum entropy clustering algorithm RMEC, as the improved version of the maximum entropy algorithm MEC [2–4], is presented to overcome MEC's drawbacks: very sensitive to outliers and uneasy to label them. Algorithm RMEC incorporates Vapnik's ɛ – insensitive loss function and the new concept of weight factors into its objective function and consequently, its new update rules are derived according to the Lagrangian optimization theory. Compared with algorithm MEC, the main contributions of algorithm RMEC exit in its much better robustness to outliers and the fact that it can effectively label outliers in the dataset using the obtained weight factors. Our experimental results demonstrate its superior performance in enhancing the robustness and labeling outliers in the dataset.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Rose K, Gurewtiz E, Fox G (1990) A deterministic annealing approach to clustering. Pattern Recogn Lett 11:589–594

    Google Scholar 

  2. Karayiannis NB (1994) MECA: Maximum entropy clustering algorithm. In: Proceedings on IEEE International conference on fuzzy systems, Orlando, pp 630–635

  3. Li RP, Mukaidono M (1995) A maximum entropy approach to fuzzy clustering. In: Proceedings on IEEE International conference fuzzy systems, Yokohama, Japan, pp 2227–2232

  4. Zhang ZH et al (2001) Maximum entropy clustering algorithm and the analysis of its global convergence performance. Science in China (Series E) 31(1):59–70

    Google Scholar 

  5. Las M, Kandel A (1999) Automated perceptions in dataset mining. Proceedings of the Eighth International Conference on Fuzzy System. Seoul, Korea. Part I pp 190–197

  6. Mendenhall W, Reinmuth JE, Beaver RJ (1993) Statistics for management and economics. Duxbury Press, Belmont

  7. Leski JM (2003) Towards a robust clustering. Fuzzy Sets Syst 137(2):191–196

    Google Scholar 

  8. Vapnik V (1998) Statistical learning theory. Wiley, New York

  9. Gill PE, Murray W, Wright MH (1981) Practical optimization. Academic, New York

  10. Huber PJ (1981) Robust statistics. Wiley, New York

  11. Wang Shitong et al (1998) Neuro-fuzzy systems and their applications. The Press of BeiJing Aeronautical University, BeiJing

  12. Steve RG (1998) Support vector machines classification and regression. Technical Report

  13. Deng Zhaohong et al. (2003) Modified min-max fuzzy neural network and function modeling. J Southern Yangtze university, No 3:75–84

    Google Scholar 

  14. Deng Zhaohong, Wang Shitong. Visual kernel perceptron. J Southern Yangtze university

  15. Wang Shitong et al. (2004) Note on the relationship between probabilistic/fuzzy clustering. J Soft Comput 8(7):523–526

    Google Scholar 

  16. Wang Shitong et al. (2004) On fuzzy morphological associative memories. IEEE Trans Fuzzy Syst 12(6):316–323

    Google Scholar 

  17. Wang Shitong et al. (2002) A new integrated clustering algorithm GFC and switching regressions. Int J Pattern Recog Artif Intell 16(4):433–446

    Google Scholar 

  18. Bezdek JC (1982) Pattern recognition with fuzzy objective function algorithms. Plenum, New York

  19. Wang Shitong et al. (2004) Fuzzy kernel clustering with outliers. Chin J Software 15(7):1021–1029

  20. Barnett V, Lewis T (1994) Outliers in statistical data. Wiley, New York

  21. Kollios G, Gunopulos D et al. (2003) Efficient biased sampling for approximate clustering and outlier detection in large data sets. IEEE Trans Knowl Data Eng 15(5):1170–1187

    Google Scholar 

  22. Kailing K, Kriegel HP et al. (2004) Clustering multi-represented objects with noise. In: Proceedings 8th Pacific-Asia Conference on PAKDD'04, Australia, pp 394–403

  23. Jaing MJ, Tseng SS et al. (2001) Two-phase clustering process for outliers detection. Pattern Recog Lett 22(6):691–700

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Engineering, Southern Yangtze University, Wuxi, China

    Wang Shitong, Deng Zhaohong & Wu Xisheng

  2. Department of Computing, HongKong Polytechnic University, HongKong, China

    Korris F.L. Chung & Deng Zhaohong

  3. School of Automation, National Defense University of Science and Technology Changsha, China

    Hu Dewen

Authors
  1. Wang Shitong
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Korris F.L. Chung
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Deng Zhaohong
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Hu Dewen
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Wu Xisheng
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Wang Shitong.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shitong, W., Chung, K., Zhaohong, D. et al. Robust maximum entropy clustering algorithm with its labeling for outliers. Soft Comput 10, 555–563 (2006). https://doi.org/10.1007/s00500-005-0517-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-005-0517-5

Keywords