Skip to main content

Advertisement

Log in

PSO-based K-Means clustering with enhanced cluster matching for gene expression data

  • ICONIP 2011
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

An integration of particle swarm optimization (PSO) and K-Means algorithm is becoming one of the popular strategies for solving clustering problem, especially unsupervised gene clustering. It is known as PSO-based K-Means clustering algorithm (PSO-KM). However, this approach causes the dimensionality of clustering problem to expand in PSO search space. The sequence of clusters represented in particle is not evaluated. This study proposes an enhanced cluster matching to further improve PSO-KM. In the proposed scheme, prior to the PSO updating process, the sequence of cluster centroids encoded in a particle is matched with the corresponding ones in the global best particle with the closest distance. On this basis, the sequence of centroids is evaluated and optimized with the closest distance. This makes particles to perform better in searching the optimum in collaborative manner. Experimental results show that this proposed scheme is more effective in reducing clustering error and improving convergence rate.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Brown P, Botstein D (1999) Exploring the new world of the genome with DNA microarrays. Nat Genet 21:33–37

    Article  Google Scholar 

  2. Brazma A, Robinson A, Cameron G, Ashburner M (2000) One-stop shop for microarray data. Nature 403:699–700

    Article  Google Scholar 

  3. Asyali MH et al (2006) Gene expression profile classification: a review. Curr Bioinform 1:55–73

    Article  Google Scholar 

  4. Dopazo J (2006) Functional interpretation of microarray experiments. OMICS 10:3

    Article  Google Scholar 

  5. Kerr G, Ruskin HJ, Crane M, Doolan P (2008) Techniques for clustering gene expression data. Comput Biol Med 38:283–293

    Article  Google Scholar 

  6. Hartigan JA, Wong MA (1979) A K-Means clustering algorithm. Appl Stat 28:126–130

    Article  Google Scholar 

  7. Du Z et al (2008) PK-Means: a new algorithm for gene clustering. Comput Biol Chem 32(4):243–247

    Article  Google Scholar 

  8. Sun J et al (2012) Gene expression data analysis with the clustering method based on an improved quantum-behaved particle swarm optimization. Eng Appl Artif Intell 25(2):376–391

    Google Scholar 

  9. Shi Y, Eberhart RC (1998) A modified particle swarm optimizer. In: Proceedings of the IEEE international conference on evolutionary computation, IEEE Press, Piscataway, NJ, pp 69–73

  10. Lam YK, Tsang PWM, Leung CS (2011) Improved gene clustering based on particle swarm optimization, K-Means, and cluster matching. In: ICONIP 2011, part I, LNCS, Springer, Heidelberg, vol 7062, pp 654–661

  11. Alizadeh AA et al (2000) Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403:503–511

    Article  Google Scholar 

  12. Spellman PT et al (1998) Comprehensive identification of cell cycle-regulated genes of the yeast. Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell 9:3273–3297

    Google Scholar 

  13. Chu S et al (1998) The transcriptional program of sporulation in budding yeast. Science 282:699–705

    Article  MATH  Google Scholar 

  14. Troyanskaya O et al (2001) Missing value estimation methods for DNA microarrays. Bioinformatics 17:520–525

    Article  Google Scholar 

Download references

Acknowledgments

The work was supported by a research grant (7002760) from City University of Hong Kong.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yau-King Lam.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lam, YK., Tsang, P.W.M. & Leung, CS. PSO-based K-Means clustering with enhanced cluster matching for gene expression data. Neural Comput & Applic 22, 1349–1355 (2013). https://doi.org/10.1007/s00521-012-0959-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-012-0959-5

Keywords

Navigation