Skip to main content
SpringerLink
Log in

On-line Robust Fuzzy Clustering for Anomalies Detection

  • Conference paper
  • First Online:
Advances in Computer Science for Engineering and Education (ICCSEEA 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 754))

Abstract

Widly-used fuzzy c-means algorithm (FCM) has been utilized, with much success, in a variety of applications. The algorithm is known as an objective function based fuzzy clustering technique that extends the use of classical k-means method to fuzzy partitions. However, one of the most important drawbacks of this method is its sensitivity to noise and outliers in data since the objective function is the sum of squared distance. New robust fuzzy clustering algorithm (RFC) for exploring of signals of different nature taking into account the presence of noise with unknown density distributions and anomalous outliers in the data being analyzed is presented in this paper. By rejection of the Euclidean distance in the objective function the insensibility to the noise and outliers in the data was archived. Our approach introduces a robust probabilistic clustering procedure and is based on a modified objective function.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Delen, D.: Real-World Data Mining: Applied Business Analytics and Decision Making. Pearson FT Press, New Jersey (2015)

    Google Scholar 

  2. Aggarwal, C.C.: A Data Mining: The Textbook. Springer, New York (2015)

    Book  Google Scholar 

  3. Larose, D.T.: Discovering Knowledge in Data: An Introduction to Data Mining. Wiley, Hoboken (2014)

    Book  Google Scholar 

  4. Yang, M.-S., Chang-Chien, S.-J., Hung, W.-L.: An unsupervised clustering algorithm for data on the unit hypersphere. Appl. Soft Comput. 42, 290–313 (2016)

    Article  Google Scholar 

  5. Dunn, J.C.: A fuzzy relative of the ISODATA process and its Use in detecting compact well-separated clusters. J. Cybern. 3(3), 32–57 (1973)

    Article  MathSciNet  Google Scholar 

  6. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithm. Plenum Press, New York (1981)

    Book  Google Scholar 

  7. Bezdek, J.C., Keller, J., Krisnapuram, R., Pal, N.R.: Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Springer, Boston (1999)

    Book  Google Scholar 

  8. Xu, R., Wunsch II, D.: Survey of clustering algorithms. IEEE Trans. Neural Netw. 16(3), 645–678 (2005)

    Article  Google Scholar 

  9. Davé, R.N.: Characterization and detection of noise in clustering. Patt. Recogn. Lett. 12(11), 657–664 (1991)

    Article  Google Scholar 

  10. Krishnapuram, R., Joshi, A., Nasraoui, O., Yi, L.: Low-complexity fuzzy relational clustering algorithms for Web mining. IEEE Trans. Fuzzy Syst. 9(4), 595–607 (2001)

    Article  Google Scholar 

  11. Bodyanskiy, Y.: Computational intelligence techniques for data analysis. In: Proceedings of the LIT 2005, vol. P-72, pp. 15–36. Gesellschaft für Informatik, Bonn (2005)

    Google Scholar 

  12. Bodyanskiy, Y., Gorshkov, Y., Kokshenov, I., Kolodyazhniy, V.: Robust recursive fuzzy clustering algorithms. In: Proceedings of the East West Fuzzy Colloqium 2005, pp. 301–308. HS Zittau/Görlitz (2005)

    Google Scholar 

  13. Tsuda, K., Senda, S., Minoh, M., Ikeda, K.: Sequential fuzzy cluster extraction and its robustness against noise. Syst. Comp. Jpn. 28(6), 10–17 (1997)

    Article  Google Scholar 

  14. Höppner, F., Klawonn, F., Kruse, R., Runkler, T.: Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition. Wiley, Chichester (1999)

    MATH  Google Scholar 

  15. Georgieva, O., Klawonn, F.: A clustering algorithm for identification of single clusters in large data sets. In: Proceedings of the East West Fuzzy Colloquium 2004, pp. 118–125. HS Zittau/Görlitz (2004)

    Google Scholar 

  16. Butkiewicz, B.S.: Robust fuzzy clustering with fuzzy data. In: Szczepaniak, P.S., Kacprzyk, J., Niewiadomski, A. (eds.) Advances in Web Intelligence, vol. 3528, pp. 76–82. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  17. Bodyanskiy, Y., Kokshenev, I., Gorshkov, Y., Kolodyazhniy, V.: Outlier resistant recursive fuzzy clustering algorithms. In: International Conference 9th Fuzzy Days in Dortmund: Computational Intelligence, Theory and Applications, pp. 647–652. Dortmund (2006)

    Google Scholar 

  18. Gorshkov, Y., Kokshenev, I., Bodyanskiy, Y., Kolodyazhniy, V., Shylo, O.: Robust recursive fuzzy clustering-based segmentation of biological time series. In: Proceedings of the 2006 International Symposium on Evolving Fuzzy Systems (EFS 2006), pp. 101–105 (2006)

    Google Scholar 

  19. Tsypkin, Y.Z.: Foundations of the Information Theory of Identification. Science, Moscow (1984). (in Russian)

    MATH  Google Scholar 

  20. Hu, Z., Bodyanskiy, Y.V., Tyshchenko, O.K., Samitova, V.O.: Fuzzy clustering data given on the ordinal scale based on membership and likelihood functions sharing. Int. J. Intell. Syst. Appl. (IJISA) 9(2), 1–9 (2017). https://doi.org/10.5815/ijisa.2017.02.01

    Google Scholar 

  21. Hu, Z., Bodyanskiy, Y.V., Tyshchenko, O.K., Samitova, V.O.: Fuzzy clustering data given in the ordinal scale. Int. J. Intell. Syst. Appl. (IJISA) 9(1), 67–74 (2017). https://doi.org/10.5815/ijisa.2017.01.07

    Google Scholar 

  22. Hu, Z., Bodyanskiy, Y.V., Tyshchenko, O.K., Samitova, V.O.: Possibilistic fuzzy clustering for categorical data arrays based on frequency prototypes and dissimilarity measures. Int. J. Intell. Syst. Appl. (IJISA) 9(5), 55–61 (2017). https://doi.org/10.5815/ijisa.2017.05.07

    Google Scholar 

  23. Hu, Z., Bodyanskiy, Y.V., Tyshchenko, O.K., Tkachov, V.M.: Fuzzy clustering data arrays with omitted observations. Int. J. Intell. Syst. Appl. (IJISA) 9(6), 24–32 (2017). https://doi.org/10.5815/ijisa.2017.06.03

    Google Scholar 

  24. Zhang, Z.: Parameter estimation techniques: a tutorial with application to conic fitting. Image Vis. Comput. 15(1), 59–76 (1997)

    Article  Google Scholar 

  25. Galvin, F., Shore, S.D.: Distance functions and topologies. Am. Math. Mon. 98(7), 620 (1991)

    Article  MathSciNet  Google Scholar 

  26. Bodyanskiy, Y., Vynokurova, O., Savvo, V., Tverdokhlib, T., Mulesa, P.: Hybrid clustering-classification neural network in the medical diagnostics of the reactive arthritis. Int. J. Intell. Syst. Appl. (IJISA) 8(8), 1–9 (2016). https://doi.org/10.5815/ijisa.2016.08.01

    Google Scholar 

  27. Coppola, C., Pacelli, T.: Approximate distances, pointless geometry and incomplete information. Fuzzy Sets Syst. 157(17), 2371–2383 (2006)

    Article  MathSciNet  Google Scholar 

  28. Perova, I., Pliss, I.: Deep hybrid system of computational intelligence with architecture adaptation for medical fuzzy diagnostics. Int. J. Intell. Syst. Appl. (IJISA) 9(7), 12–21 (2017). https://doi.org/10.5815/ijisa.2017.07.02

    Google Scholar 

  29. Li, S.Z.: Markov Random Field Modeling in Computer Vision. Springer, Tokyo (1995)

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Kharkiv National University of Radio Electronics, Kharkiv, Ukraine

    Yevgeniy Bodyanskiy

  2. Kherson National Technical University, Kherson, Ukraine

    Oleksii Didyk

Authors
  1. Yevgeniy Bodyanskiy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Oleksii Didyk
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Oleksii Didyk .

Editor information

Editors and Affiliations

  1. School of Educational Information Technology, Central China Normal University, Wuhan, Hubei, China

    Zhengbing Hu

  2. Mechanical Engineering Research Institute, Russian Academy of Sciences, Moscow, Russia

    Sergey Petoukhov

  3. Faculty of Applied Mathematics, National Technical University of Ukraine “Igor Sikorsky Kiev Polytechnic Institute”, Kiev, Ukraine

    Ivan Dychka

  4. Halmos College of Natural Sciences and Oceanography, Nova Southeastern University, Ft. Lauderdale, Florida, USA

    Matthew He

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bodyanskiy, Y., Didyk, O. (2019). On-line Robust Fuzzy Clustering for Anomalies Detection. In: Hu, Z., Petoukhov, S., Dychka, I., He, M. (eds) Advances in Computer Science for Engineering and Education. ICCSEEA 2018. Advances in Intelligent Systems and Computing, vol 754. Springer, Cham. https://doi.org/10.1007/978-3-319-91008-6_40

Download citation

Publish with us

Policies and ethics

Search

Navigation