Skip to main content
SpringerLink
Log in
Book cover

7th International Conference on Practical Applications of Computational Biology & Bioinformatics pp 15–22Cite as

A Clustering Framework Applied to DNA Microarray Data

  • Conference paper

  • 783 Accesses

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 222))

Abstract

This paper presents a case study to show the competence of our evolutionary framework for cluster analysis of DNA microarray data. The proposed framework joins a genetic algorithm for hierarchical clustering with a set of visual components of cluster tasks given by a tool. The cluster visualization tool allows us to display different views of clustering results as a means of cluster visual validation. The results of the genetic algorithm for clustering have shown that it can find better solutions than the other methods for the selected data set. Thus, this shows the reliability of the proposed framework.

This is a preview of subscription content, 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   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice Hall, Englewood Cliffs (1998)

    Google Scholar 

  2. Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data. An Introduction to Clustering Analysis. John Wiley & Sons, Inc., Hoboken (2005)

    Google Scholar 

  3. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison Wesley Longman, Inc. (1989)

    Google Scholar 

  4. Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press Edition (1992)

    Google Scholar 

  5. Laszlo, M., Mukherjee, S.: A genetic algorithm that exchanges neighboring centers for k-means clustering. Pattern Recognition Letture 28, 2359–2366 (2007)

    Article  Google Scholar 

  6. Bourne, P.E., Wissig, H.: Structural Bioinformatics. Wiley-Liss, Inc., Hoboken (2003)

    Book  Google Scholar 

  7. Castellanos-Garzón, J.A., Díaz, F.: An evolutionary computational model applied to cluster analysis of DNA microarray data. Expert Systems with Applications 40, 2575–2591 (2013)

    Article  Google Scholar 

  8. Castellanos-Garzón, J.A., García, C.A., Novais, P., Díaz, F.: A visual analytics framework for cluster analysis of DNA microarray data. Expert Systems with Applications 40, 758–774 (2013)

    Article  Google Scholar 

  9. Jiang, D., Tang, C., Zhang, A.: Cluster analysis for gene expression data: A survey. IEEE Transactions on Knowledge and Data Engineering 16(11), 1370–1386 (2004)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University School of Computer Science, Campus Maria Zambrano, University of Valladolid, Plaza Alto de los Leones, 1, 40005, Segovia, Spain

    José A. Castellanos-Garzón & Fernando Díaz

Authors
  1. José A. Castellanos-Garzón
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fernando Díaz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to José A. Castellanos-Garzón .

Editor information

Editors and Affiliations

  1. , Department of Software Engineering, Universiti Teknologi Malaysia, Skudai, Johor, 81310, Malaysia

    Mohd Saberi Mohamad

  2. , Department of Information Engineering, Università di Padua, Via Gradenigo 6, Padua, 35122, Italy

    Loris Nanni

  3. , Dep. Informática / CCTC, Universidade do Minho, Braga, 4710 - 057, Portugal

    Miguel P. Rocha

  4. , Department of Informatics, University of Vigo, Ourense, 32004, Spain

    Florentino Fdez-Riverola

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer International Publishing Switzerland

About this paper

Cite this paper

Castellanos-Garzón, J.A., Díaz, F. (2013). A Clustering Framework Applied to DNA Microarray Data. In: Mohamad, M., Nanni, L., Rocha, M., Fdez-Riverola, F. (eds) 7th International Conference on Practical Applications of Computational Biology & Bioinformatics. Advances in Intelligent Systems and Computing, vol 222. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00578-2_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-00578-2_3

  • Publisher Name: Springer, Heidelberg

  • Print ISBN: 978-3-319-00577-5

  • Online ISBN: 978-3-319-00578-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation