Skip to main content
SpringerLink
Log in

A Generalization of Fuzzy c-Means with Variables Controlling Cluster Size

  • Conference paper
  • First Online:
Modeling Decisions for Artificial Intelligence (MDAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13890))

  • 340 Accesses

Abstract

This study constructs two general fuzzy clustering algorithms with a cluster size controller. The first algorithm includes the standard fuzzy c-means (SFCM), modified SFCM, and generalized fuzzy c-means, and the second one includes the entropy-regularized fuzzy c-means (EFCM), modified EFCM (mEFCM), and regularized fuzzy c-means (RFCM). Furthermore, the results of this study demonstrate that the behavior of the fuzzy classification functions of the first proposed algorithm at points far from clusters are similar to that for mSFCM, and those of the second one are similar to those for EFCM, mEFCM, and RFCM. some conventional clustering algorithms.

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 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
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

Similar content being viewed by others

References

  1. MacQueen, J.B.: Some methods of classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)

    Google Scholar 

  2. Bezdek, J.: Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York (1981)

    Google Scholar 

  3. Miyamoto, S., Mukaidono, M.: Fuzzy c-means as a regularization and maximum entropy approach. In: Proceedings of the 7th International Fuzzy Systems Association World Congress (IFSA1997), Vol. 2, pp. 86–92 (1997)

    Google Scholar 

  4. Miyamoto, S., Ichihashi, H., Honda, K.: Algorithms for Fuzzy Clustering, Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78737-2

  5. Kanzawa, Y., Miyamoto, S.: Generalized fuzzy c-means clustering and its property of fuzzy classification function. JACIII 25(1), 73–82 (2021)

    Article  Google Scholar 

  6. Kanzawa, Y., Miyamoto, S.: Regularized fuzzy c-means clustering and its behavior at point of infinity. JACIII 23(3), 485–492 (2019)

    Article  Google Scholar 

  7. Miyamoto, S., Kurosawa, N.: Controlling cluster volume sizes in fuzzy c-means clustering. In: Proceedings SCIS &ISIS2004, pp 1–4 (2004)

    Google Scholar 

  8. Ichihashi, H., Honda, K., Tani, N.: Gaussian mixture pdf approximation and fuzzy c-means clustering with entropy regularization. In: Proceedings of the 4th Asian Fuzzy System Symposium, pp. 217–221 (2000)

    Google Scholar 

  9. Komazaki, Y., Miyamoto, S.: Variables for controlling cluster sizes on fuzzy c-means. In: Torra, V., Narukawa, Y., Navarro-Arribas, G., Megías, D. (eds.) MDAI 2013. LNCS (LNAI), vol. 8234, pp. 192–203. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41550-0_17

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Shibaura Institute of Technology, Tokyo, Japan

    Yuchi Kanzawa

Authors
  1. Yuchi Kanzawa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuchi Kanzawa .

Editor information

Editors and Affiliations

  1. Umeå University, Umeå, Sweden

    Vicenç Torra

  2. Tamagawa University, Tokyo, Japan

    Yasuo Narukawa

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kanzawa, Y. (2023). A Generalization of Fuzzy c-Means with Variables Controlling Cluster Size. In: Torra, V., Narukawa, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2023. Lecture Notes in Computer Science(), vol 13890. Springer, Cham. https://doi.org/10.1007/978-3-031-33498-6_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-33498-6_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-33497-9

  • Online ISBN: 978-3-031-33498-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation