Skip to main content
SpringerLink
Log in

I-Fuzzy Partitions for Representing Clustering Uncertainties

  • Conference paper
Artificial Intelligence and Soft Computing (ICAISC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6113))

Included in the following conference series:

Abstract

In a recent paper we introduced intuitionistic fuzzy partitions or interval-valued fuzzy partitions as a way to represent the uncertainty of fuzzy clustering. In this paper we reconsider these definitions so that these fuzzy partitions can be used to represent other uncertainties on the clustering processes.

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Atanassov, K.T.: Intuitionistic fuzzy sets. VII ITKR’s Session, Sofia (1983) (in Bulgarian)

    Google Scholar 

  2. Atanassov, K.T.: Intuitionistic fuzzy sets. Fuzzy Sets and Systems 20, 87–96 (1986)

    Article  MATH  MathSciNet  Google Scholar 

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

    MATH  Google Scholar 

  4. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, Berlin (2001)

    MATH  Google Scholar 

  5. Höppner, F., Klawonn, F., Kruse, R., Runkler, T.: Fuzzy cluster analysis. Wiley, Chichester (1999)

    MATH  Google Scholar 

  6. Hwang, C., Rhee, F.C.-H.: Uncertain Fuzzy Clustering: Interval Type-2 Fuzzy Approach to C-Means. IEEE Trans. on Fuzzy Systems 15(1), 107–120 (2007)

    Article  Google Scholar 

  7. Ladra, S., Torra, V.: On the comparison of generic information loss measures and cluster-specific ones. Intl. J. of Unc., Fuzz. and Know. Based Syst. 16(1), 107–120 (2008)

    Article  Google Scholar 

  8. Min, J.-H., Shim, E.-A., Rhee, F.C.-H.: An Interval Type-2 Fuzzy PCM Algorithm for Pattern Recognition. In: Proc. of FUZZ-IEEE 2009, Korea, pp. 480–483 (2009)

    Google Scholar 

  9. Miyamoto, S., Ichihashi, H., Honda, K.: Algorithms for fuzzy clustering. Springer, Heidelberg (2008)

    MATH  Google Scholar 

  10. Torra, V., Endo, Y., Miyamoto, S.: On the comparison of some fuzzy clustering methods for privacy preserving data mining: towards the development of specific information loss measures. Kybernetika 45(3), 548–560 (2009)

    MATH  MathSciNet  Google Scholar 

  11. Torra, V., Miyamoto, S.: On Intuitionistic Fuzzy Partitions (2009) (manuscript)

    Google Scholar 

  12. Torra, V., Miyamoto, S., Endo, Y., Domingo-Ferrer, J.: On intuitionistic fuzzy clustering for its application to privacy. In: Proc. FUZZ-IEEE/WCCI 2008, Hong-Kong, China, pp. 1042–1048 (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IIIA, Institut d’Investigació en Intel·ligència Artificial, CSIC, Spanish Council for Scientific Research, Campus de Bellaterra, 08193, Bellaterra, Catalonia, Spain

    Vicenç Torra

  2. School of Electrical Engineering and Computer Science, Hanyang University, Korea

    Ji-Hee Min

Authors
  1. Vicenç Torra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ji-Hee Min
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. Academy of Humanities and Economics in Łódź,, ul. Rewolucji 1905 nr 64, Łódź, Poland

    Rafał Scherer

  3. Institute of Automatics,, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  4. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Initiative in Soft Computing (BISC), 94720-1776, Berkeley, CA,  

    Lotfi A. Zadeh

  5. Computational Intelligence Laboratory Department of Electrical and Computer Engineering, University of Louisville, 40292, Louisville, KY

    Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Torra, V., Min, JH. (2010). I-Fuzzy Partitions for Representing Clustering Uncertainties. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13208-7_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-13208-7_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13207-0

  • Online ISBN: 978-3-642-13208-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation