Skip to main content
SpringerLink
Log in

On the Use of Variable-Size Fuzzy Clustering for Classification

  • Conference paper
Modeling Decisions for Artificial Intelligence (MDAI 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3885))

Abstract

Hard c-means can be used for building classifiers in supervised machine learning. For example, in a n-class problem, c clusters are built for each of the classes. This results into n . c centroids. Then, new examples can be classified according to the nearest centroid.

In this work we consider the problem of building classifiers using fuzzy clustering techniques. In particular, we consider the use of fuzzy c-means, as well as some variations. Namely, fuzzy c-means with variable size and entropy based fuzzy c-means.

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. Barreto Bezerra, G., Nunes de Castro, L.: Bioinformatics Data Analysis Using an Artificial Immune Network. In: Timmis, J., Bentley, P.J., Hart, E. (eds.) ICARIS 2003. LNCS, vol. 2787, pp. 22–33. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

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

    Book  MATH  Google Scholar 

  3. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, Chichester (2000)

    MATH  Google Scholar 

  4. Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95, 14863–14868 (1998)

    Article  Google Scholar 

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

    Book  MATH  Google Scholar 

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

    MATH  Google Scholar 

  7. Ichihashi, H., Honda, K., Tani, N.: Gaussian mixture PDF approximation and fuzzy c-means clustering with entropy regularization. In: Proc. of the 4th Asian Fuzzy System Symposium, Tsukuba, Japan, May 31-June 3, pp. 217–221 (2000)

    Google Scholar 

  8. Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)

    MATH  Google Scholar 

  9. Miyamoto, S., Umayahara, K.: Methods in Hard and Fuzzy Clustering. In: Liu, Z.-Q., Miyamoto, S. (eds.) Soft Computing and Human-Centered Machines, pp. 85–129. Springer, Tokyo (2000)

    Chapter  Google Scholar 

  10. Miyamoto, S., Mukaidono, M.: Fuzzy c - means as a regularization and maximum entropy approach. In: Proc. of the 7th International Fuzzy Systems Association World Congress (IFSA 1997), Prague, Chech, June 25-30, vol. II, pp. 86–92 (1997)

    Google Scholar 

  11. Miyamoto, S.: Introduction to fuzzy clustering, Morikita (ed.), Japan (1999) (in Japanese)

    Google Scholar 

  12. Miyamoto, S., Umayahara, K.: Fuzzy c-means with variables for cluster sizes. In: 16th Fuzzy System Symposium, Akita, September 6-8, pp. 537–538 (2000) (in Japanese)

    Google Scholar 

  13. http://rana.lbl.gov/EisenData.htm

Download references

Author information

Authors and Affiliations

  1. Institut d’Investigació en Intel.ligència Artificial, Campus de Bellaterra, 08193, Bellaterra, Catalonia, Spain

    Vicenç Torra

  2. Department of Risk Engineering, School of Systems and Information Engineering, University of Tsukuba, 305-8573, Ibaraki, Japan

    Sadaaki Miyamoto

Authors
  1. Vicenç Torra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sadaaki Miyamoto
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. IIIA, Artificial Intelligence Research Institute CSIC, Spanish National Research Council, Campus UAB s/n, Catalonia, 08193, Bellaterra, Spain

    Vicenç Torra

  2. Toho Gakuen, 3-1-10 Naka, 186-0004, Tokyo, Kunitachi, Japan

    Yasuo Narukawa

  3. Department of Computer Science and Mathematics Intelligent Technologies for Advanced Knowledge Acquisition Research Group,, University Rovira i Virgili, Av. Països Catalans, 26., 43007, Tarragona, Catalonia, Spain

    Aïda Valls

  4. Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, UNESCO Chair in Data Privacy, Av. Països Catalans 26, E-43007, Tarragona, Catalonia, Spain

    Josep Domingo-Ferrer

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Torra, V., Miyamoto, S. (2006). On the Use of Variable-Size Fuzzy Clustering for Classification. In: Torra, V., Narukawa, Y., Valls, A., Domingo-Ferrer, J. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2006. Lecture Notes in Computer Science(), vol 3885. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11681960_35

Download citation

  • DOI: https://doi.org/10.1007/11681960_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-32780-6

  • Online ISBN: 978-3-540-32781-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation