Skip to main content
SpringerLink
Log in

Forced Strategy Differential Evolution Used for Data Clustering

  • Chapter
  • First Online:
Book cover Metaheuristics for Data Clustering and Image Segmentation

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 152))

  • 410 Accesses

Abstract

This chapter introduces another approach of Differential Evolution algorithm named as FSDE—Forced Strategy Differential Evolution. FSDE uses two control parameters: a constant parameter and a varying parameter. By using two control parameters, the efficiency of FSDE improves greatly. This variant is then applied on clustering of data.

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 EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31(8), 651–666 (2010)

    Article  Google Scholar 

  2. Bezdek J.C., Ehrlich R., Full W.:FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10(2–3), 191–203 (1984)

    Article  Google Scholar 

  3. MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, no. 14. pp. 281–297 (1967)

    Google Scholar 

  4. Coello, C.A.C., David, A.V.V., Gary, B.L.: Evolutionary algorithms for solving multi-objective problems, vol. 242. Kluwer Academic, New York (2002)

    Book  Google Scholar 

  5. Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)

    Article  MathSciNet  Google Scholar 

  6. Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 2, 224–227 (1979)

    Article  Google Scholar 

  7. Caliński, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat. Theor. Meth. 3(1), 1–27 (1974)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Information Technology, University College of Bahrain, Manama, Bahrain

    Meera Ramadas

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

Authors
  1. Meera Ramadas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ajith Abraham
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Meera Ramadas .

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ramadas, M., Abraham, A. (2019). Forced Strategy Differential Evolution Used for Data Clustering. In: Metaheuristics for Data Clustering and Image Segmentation. Intelligent Systems Reference Library, vol 152. Springer, Cham. https://doi.org/10.1007/978-3-030-04097-0_5

Download citation

Publish with us

Policies and ethics

Search

Navigation