Skip to main content
SpringerLink
Log in

Variable Based Fuzzy Blocking Regression Model

  • Conference paper
Knowledge-Based Intelligent Information and Engineering Systems (KES 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4693))

Abstract

A fuzzy clustering based blocking regression model is proposed considering fuzzy intercepts and weights for each variable. The fuzzy intercepts and weights are obtained by using two fuzzy clustering results. One is a conventional fuzzy clustering over all variables and the other uses variable based fuzzy clustering. By involving the fuzzy clustering results, we can implement a regression model including nonlinear spatial data structures which are observed in a space consisting of all of the variables and a space consisting of each variable.

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 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bezdek, J.C., Keller, J., Krisnapuram, R., Pal, N.R.: Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Kluwer Academic Publishers, Dordrecht (1999)

    Book  MATH  Google Scholar 

  2. Brunsdon, C., Fotheringham, S., Charlton, M.: Geographically Weighted Regression Modeling Spatial Non-Stationarity. Journal of the Royal Statistical Society 47, 431–443 (1998)

    Google Scholar 

  3. Draper, N.R., Smith, H.: Applied Regression Analysis. John Wiley & Sons, Chichester (1966)

    MATH  Google Scholar 

  4. Fisher, R.A.: The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics 7, 179–188 (1936)

    Article  Google Scholar 

  5. Hathaway, R.J., Bezdek, J.C.: Switching Regression Models and Fuzzy Clustering. IEEE Trans. Fuzzy Syst. 1(3), 195–204 (1993)

    Article  Google Scholar 

  6. Sato-Ilic, M., Jain, L.C.: Innovations in Fuzzy Clustering. Springer, Heidelberg (2006)

    MATH  Google Scholar 

  7. Sato-Ilic, M.: Weighted Fuzzy Clustering on Subsets of Variables. In: International Symposium on Signal Processing and its Applications with the International Conference on Information Sciences, Signal Processing and its Applications (2007)

    Google Scholar 

  8. Sato-Ilic, M.: Regression Analysis Considering Fuzzy Block Intercepts. In: International Symposium Management Engineering, T03 (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki 305-8573, Japan

    Mika Sato-Ilic

Authors
  1. Mika Sato-Ilic
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dipartimento di Scienze dell’Informazione, Università degli Studi di Milano, Via Comelico 39/41, 20135, Milano, Italy

    Bruno Apolloni

  2. Centre for SMART Systems, School of Engineering, University of Brighton, BN2 4GJ, Brighton, UK

    Robert J. Howlett

  3. Knowledge-Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, SA 5095, Adelaide, Australia

    Lakhmi Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Sato-Ilic, M. (2007). Variable Based Fuzzy Blocking Regression Model. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4693. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74827-4_66

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-74827-4_66

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74826-7

  • Online ISBN: 978-3-540-74827-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation