Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Clustering Problems for More Useful Benchmarking of Optimization Algorithms

  • Conference paper
Simulated Evolution and Learning (SEAL 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8886))

Included in the following conference series:

Abstract

This paper analyses the data clustering problem from the continuous black-box optimization point of view and proposes methodological guidelines for a standard benchmark of clustering problem instances. Clustering problems have been used many times in the literature to evaluate evolutionary, metaheuristic and other global optimization algorithms. However much of this work has occurred independently and the various experimental methodologies used have produced results which tend to be incomparable and provide little collective wisdom as to the difficulty of the problems used, or an objective measure for comparing and evaluating the performance of algorithms. This paper surveys some of the clustering literature and results to identify issues relevant for benchmarking. A set of 27 problem instances ranging from 4-D to 40-D and based on three well-known datasets is identified. To establish some pilot results on this benchmark set, experiments are presented for the Covariance Matrix Adaptation-Evolution Strategy and several other standard algorithms. A web-repository has also been created for this problem set to facilitate better experimental evaluations of algorithms.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /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
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.

Similar content being viewed by others

References

  1. Blake, C., Keogh, E., Merz, C.J.: UCI repository of machine learning databases(1998), http://www.ics.uci.edu/~mlearn/MLRepository.html (retrieved)

  2. Brimberg, J., Hansen, P., Mladenovic, N., Taillard, E.D.: Improvements and comparison of heuristics for solving the uncapacitated multisource Weber problem. Operations Research 48(3), 444–460 (2000)

    Article  Google Scholar 

  3. Chang, D.-X., Zhang, X.-D., Zheng, C.-W.: A genetic algorithm with gene rearrangement for k-means clustering. Pattern Recognition 42(7), 1210–1222 (2009)

    Article  Google Scholar 

  4. Du Merle, O., Hansen, P., Jaumard, B., Mladenovic, N.: An interior point algorithm for minimum sum-of-squares clustering. SIAM Journal on Scientific Computing 21(4), 1485–1505 (2000)

    Article  MATH  Google Scholar 

  5. Fathian, M., Amiri, B., Maroosi, A.: Application of honey-bee mating optimization algorithm on clustering. Applied Mathematics and Computation 190(2), 1502–1513 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  6. Kao, Y., Cheng, K.: An ACO-based clustering algorithm. In: Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., Stützle, T. (eds.) ANTS 2006. LNCS, vol. 4150, pp. 340–347. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  7. Liu, R., Shen, Z., Jiao, L., Zhang, W.: Immunodomaince based clonal selection clustering algorithm. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–7 (2010)

    Google Scholar 

  8. Maulik, U., Bandyopadhyay, S.: Genetic algorithm-based clustering technique. Pattern Recognition 33(9), 1455–1465 (2000)

    Article  Google Scholar 

  9. Salhi, S., Gamal, M.D.H.: A genetic algorithm based approach for the uncapacitated continuous location-allocation problem. Annals of Operations Research 123, 230–222 (2003)

    Google Scholar 

  10. Shelokar, P.S., Jayaraman, V.K., Kulkarni, B.D.: An ant colony approach for clustering. Analytica Chimica Acta 509(2), 187–195 (2004)

    Article  Google Scholar 

  11. Steinley, D.: K-means clustering: A half-century synthesis. British Journal of Mathematical and Statistical Psychology 59, 1–34 (2006)

    Article  MathSciNet  Google Scholar 

  12. Taherdangkoo, M., Shirzadi, M.H., Yazdi, M., Bagheri, M.H.: A robust clustering method based on blind, naked mole-rats (bnmr) algorithm. Swarm and Evolutionary Computation 10, 1–11 (2013)

    Article  Google Scholar 

  13. Xu, R., Wunsch II., D.: Survey of clustering algorithms. IEEE Transactions on Neural Networks 16(3), 645–678 (2005)

    Article  Google Scholar 

  14. Ye, F., Chen, C.-Y.: Alternative kpso-clustering algorithm. Tamkang Journal of Science and Engineering 8(2), 165 (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, 4072, Australia

    Marcus Gallagher

Authors
  1. Marcus Gallagher
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Otago University, Dunedin, New Zealand

    Grant Dick

  2. Victoria University of Welling, New Zealand

    Will N. Browne

  3. University of Otago, Dunedin, New Zealand

    Peter Whigham

  4. Unitec Institute of Technology, Victoria University of Wellington, New Zealand

    Mengjie Zhang

  5. Le Quy Don Technical University, Hanoi, Vietnam

    Lam Thu Bui

  6. Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, 599-8531, Sakai, Osaka, Japan

    Hisao Ishibuchi

  7. Department of Computing, University of Surrey, GU2 7XH, Guildford, Surrey, UK

    Yaochu Jin

  8. RMIT University, Melbourne, Australia

    Xiaodong Li

  9. Department of Electrical & Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  10. Indian Institute of Information Technology and Management, Gwalior, India

    Pramod Singh

  11. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore, Singapore

    Kay Chen Tan

  12. USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI), School of Computer Science and Technology, University of Science and Technology of China, 230027, Hefei, China

    Ke Tang

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Gallagher, M. (2014). Clustering Problems for More Useful Benchmarking of Optimization Algorithms. In: Dick, G., et al. Simulated Evolution and Learning. SEAL 2014. Lecture Notes in Computer Science, vol 8886. Springer, Cham. https://doi.org/10.1007/978-3-319-13563-2_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-13563-2_12

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13562-5

  • Online ISBN: 978-3-319-13563-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation