Skip to main content
SpringerLink
Log in
SpringerLink
Log in

On Fuzzy Clustering of Data Streams with Concept Drift

  • Conference paper
Artificial Intelligence and Soft Computing (ICAISC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7268))

Included in the following conference series:

Abstract

In the paper the clustering algorithms based on fuzzy set theory are considered. Modifications of the Fuzzy C-Means and the Possibilistic C-Means algorithms are presented, which adjust them to deal with data streams. Since data stream is of infinite size, it has to be partitioned into chunks. Simulations show that this partitioning procedure does not affect the quality of clustering results significantly. Moreover, properly chosen weights can be assigned to each data element. This modification allows the presented algorithms to handle concept drift during simulations.

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aggarwal, C.: Data Streams: Models and Algorithms. Springer, LLC (2007)

    MATH  Google Scholar 

  2. Aggarwal, C., Han, J., Wang, J., Yu, P.S.: A Framework for Clustering Evolving Data Streams. In: Proc. of the 29th Conference on Very Large Data Bases, Berlin, Germany (2003)

    Google Scholar 

  3. Aggarwal, C., Han, J., Wang, J., Yu, P.S.: A Framework for Projected Clustering of High Diensional Data Streams. In: Proc. of the 30th Conference on Very Large Data Bases, Toronto, Canada (2003)

    Google Scholar 

  4. Babuska, R.: Fuzzy Modeling for Control. Kluwer Academic Press, Dordrecht (1998)

    Book  Google Scholar 

  5. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers, Norwell (1981)

    MATH  Google Scholar 

  6. Bifet, A.: Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams. IOS Press BV, Netherlands (2010)

    MATH  Google Scholar 

  7. Dunn, J.C.: A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters. Cybernetics and Systems 3(3), 32–57 (1973)

    Article  MathSciNet  MATH  Google Scholar 

  8. Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Proc. of 2nd International Confrence on Knowledge Discovery and Data Mining, pp. 226–231. AAAI Press (1996)

    Google Scholar 

  9. Fisher, D.H.: Knowledge Acquisition via Incremental Conceptual Clustering. Machine Learning 2(2), 139–172 (1987)

    Google Scholar 

  10. Gath, I., Geva, A.B.: Unsupervised Optimal Fuzzy Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 11(7), 773–781 (1989)

    Article  Google Scholar 

  11. Guha, S., Mishra, N., Motwani, R., O’Callaghan, L.: Clustering Data Streams. In: Proc. of 41st Annual Symposium on Foundations of Computer Science, Redondo Beach, CA, USA (2000)

    Google Scholar 

  12. Hore, P., Hall, L.O., Goldgof, D.B.: Single Pass Fuzzy C Means. In: Proc. of the IEEE International Conference on Fuzzy Systems, London, July 23-26 (2007)

    Google Scholar 

  13. Khalilian, M., Mustapha, N.: Data Stream Clustering: Challenges and Issues. In: Proc. of the International Multiconference of Engineers and Computer Scientists, HongKong, vol. I (2010)

    Google Scholar 

  14. Krishnapuram, R., Keller, J.M.: A Possibilisic Approach to Clustering. IEEE Transactions on Fuzzy Systems 1(2), 98–110 (1993)

    Article  Google Scholar 

  15. McQueen, J.B.: Some Methods for Classification and Analysis of Multivariate Observations. In: Proc. of 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press, Berkeley (1967)

    Google Scholar 

  16. Miyamoto, S., Ichihashi, H., Honda, K.: Algorithms for Fuzzy Clustering. Springer, Heidelberg (2008)

    MATH  Google Scholar 

  17. Nowicki, R.: Nonlinear modelling and classification based on the MICOG defuzzifications. Journal of Nonlinear Analysis, Series A: Theory, Methods and Applications 7(12), 1033–1047 (2009)

    Article  Google Scholar 

  18. Rutkowski, L.: The real-time identification of time-varying systems by nonparametric algorithms based on the Parzen kernels. International Journal of Systems Science 16, 1123–1130 (1985)

    Article  MATH  Google Scholar 

  19. Rutkowski, L.: Sequential pattern recognition procedures derived from multiple Fourier series. Pattern Recognition Letters 8, 213–216 (1988)

    Article  MATH  Google Scholar 

  20. Rutkowski, L.: An application of multiple Fourier series to identification of multivariable nonstationary systems. International Journal of Systems Science 20(10), 1993–2002 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  21. Rutkowski, L.: Nonparametric learning algorithms in the time-varying environments. Signal Processing 18, 129–137 (1989)

    Article  MathSciNet  Google Scholar 

  22. Rutkowski, L.: Computational Intelligence. Springer (2008)

    Google Scholar 

  23. Rutkowski, L., Cpałka, K.: A general approach to neuro - fuzzy systems. In: Proceedings of the 10th IEEE International Conference on Fuzzy Systems, Melbourne, December 2-5, vol. 3, pp. 1428–1431 (2001)

    Google Scholar 

  24. Rutkowski, L., Cpałka, K.: A neuro-fuzzy controller with a compromise fuzzy reasoning. Control and Cybernetics 31(2), 297–308 (2002)

    MATH  Google Scholar 

  25. Scherer, R.: Boosting Ensemble of Relational Neuro-fuzzy Systems. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 306–313. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  26. Scherer, R.: Neuro-fuzzy Systems with Relation Matrix. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010. LNCS (LNAI), vol. 6113, pp. 210–215. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  27. Starczewski, J., Rutkowski, L.: Interval type 2 neuro-fuzzy systems based on interval consequents. In: Rutkowski, L., Kacprzyk, J. (eds.) Neural Networks and Soft Computing, pp. 570–577. Physica-Verlag, Springer-Verlag Company, Heidelberg, New York (2003)

    Google Scholar 

  28. Starczewski, J.T., Rutkowski, L.: Connectionist Structures of Type 2 Fuzzy Inference Systems. In: Wyrzykowski, R., Dongarra, J., Paprzycki, M., Waśniewski, J. (eds.) PPAM 2001. LNCS, vol. 2328, pp. 634–642. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  29. Vivekanandan, P., Nedunchezhian, R.: Mining Rules of Concept Drift Using Genetic Algorithm. Journal of Artificial Inteligence and Soft Computing Research 1(2), 135–145 (2011)

    Google Scholar 

  30. Xie, X.L., Beni, G.: A Validity Measure for Fuzzy Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(4), 841–846 (1991)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Czestochowa University of Technology, Armii Krajowej 36, 42-200, Czestochowa, Poland

    Maciej Jaworski, Piotr Duda & Lena Pietruczuk

Authors
  1. Maciej Jaworski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Piotr Duda
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lena Pietruczuk
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, Computer Science Division, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Jaworski, M., Duda, P., Pietruczuk, L. (2012). On Fuzzy Clustering of Data Streams with Concept Drift. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7268. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29350-4_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-29350-4_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29349-8

  • Online ISBN: 978-3-642-29350-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation