Movatterモバイル変換


[0]ホーム

URL:


Skip to main content

Advertisement

Springer Nature Link
Log in

Data Clustering Based on Teaching-Learning-Based Optimization

  • Conference paper

Part of the book series:Lecture Notes in Computer Science ((LNTCS,volume 7077))

  • 1886Accesses

  • 77Citations

Abstract

A new efficient optimization method, called ‘Teaching–Learning-Based Optimization (TLBO)’, has been proposed very recently for the optimization of mechanical design problems. This paper proposes a new approach to using TLBO to cluster data. It is shown how TLBO can be used to find the centroids of a user specified number of clusters. The new TLBO algorithms are evaluated on some datasets and compared to the performance of K-means and PSO clustering. Results show that TLBO clustering techniques have much potential.

This is a preview of subscription content,log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 5719
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 7149
Price includes VAT (Japan)
  • 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. van der Merwe, D.W., Engelbrecht, A.P.: Data Clustering using Particle Swarm Optimization. IEEE Evolutionary Computation 1, 215–220 (2003), doi:10.1109/CEC.2003.1299577

    Google Scholar 

  2. Forgy, E.: Cluster Analysis of Multivariate Data, Efficicncy versus Interpretability of Classification. Biometrics 2, 768–769 (1965)

    Google Scholar 

  3. Hartigan, J.A.: Clustering Algorithms. John Wiley EL Sons, New York (1975)

    MATH  Google Scholar 

  4. Ball, G., Hall, D.: A Clustering Technique for Summariring Multivariate Data. Behavioral Science 12, 153–155 (1967)

    Article  Google Scholar 

  5. Fausett, L.V.: Fundamentals of Neural Networks. Prentice Hall (1994)

    Google Scholar 

  6. Rao, R.V., Savsani, V.J., Vakharia, D.P.: Teaching–learning-based optimization: A novelmethod for constrained mechanical design optimization problems. Computer-Aided Design 43, 303–315 (2011)

    Article  Google Scholar 

  7. Kcnncdy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of the lEEE International Joint Con-Science on Neural Networks, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  8. Kennedy, J., Eherhart, R.C., Shi, Y.: Swarm Intelligence. Morgan Kaufmann (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Anil Neerukonda Institute of Technology and Sciences, Vishakapatnam, India

    Suresh Chandra Satapathy

  2. Majhighariani Institute of Technology and Sciences, Rayagada, India

    Anima Naik

Authors
  1. Suresh Chandra Satapathy
  2. Anima Naik

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, IIT, Delhi, India

    Bijaya Ketan Panigrahi

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore

    Ponnuthurai Nagaratnam Suganthan

  3. Department of Electronics and Telecommunications, Jadavpur University, 700032, Kolkata, India

    Swagatam Das

  4. ANITS, Visakhapatnam, India

    Suresh Chandra Satapathy

Rights and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Satapathy, S.C., Naik, A. (2011). Data Clustering Based on Teaching-Learning-Based Optimization. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2011. Lecture Notes in Computer Science, vol 7077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27242-4_18

Download citation

Publish with us

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 5719
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 7149
Price includes VAT (Japan)
  • 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


[8]ページ先頭

©2009-2025 Movatter.jp