Skip to main content

Advertisement

Springer Nature Link
Log in

Improving pattern discovery and visualisation with self-adaptive neural networks through data transformations

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

The ability to reveal the relevant patterns in an intuitively attractive way through incremental learning makes self-adaptive neural networks (SANNs) a power tool to support pattern discovery and visualisation. Based on the combination of the information related to both the shape and magnitude of the data, this paper introduces a SANN, which implements new similarity matching criteria and error accumulation strategies for network growth. It was tested on two datasets including a real biological gene expression dataset. The results obtained have demonstrated several significant features exhibited by the proposed SANN model for improving pattern discovery and visualisation.

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

Access this article

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

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Wang JTL, Shapiro BA, Shasha D (1999) Pattern discovery in biomolecular data: tools, techniques, and applications. Oxford University Press, New York

    Google Scholar 

  2. Shalizi CR, Crutchfield JP (2000) Pattern discovery and computational mechanics, Santa Fe Insitute Working Paper 00-01-008, Available at: arXiv.org/abs/cs.LG/0001027

  3. Pleasance ED, Jones SJM (2005) Evaluation of SAGE tags for Ttranscriptome study. In: Wang SM (ed) SAGE: current technologies and applications, Horizon Scientific Press, Norwich, pp 1–27

  4. Polyak K, Riggins GJ (2001) Gene discovery using the serial analysis of gene expression technique: implications for cancer research. J Clin Oncol 19(11):2948–2958

    Google Scholar 

  5. Brodlie KW, Carpenter LA, Earnshaw RA, Gallop JR, Hubbard RJ, Mumford AM, Osland CD, Quarendon P (eds) (1992) Scientific visualisation, techniques and applications, Springer, New York

  6. Grinstein GG, Ward MO (2002) Introduction to data visualisation. In: Fayyad U, Grinstein GG, Wierse A (eds) Information visualisation in data mining and knowledge discovery, Morgan Kaufmann Publishers, San Francisco, pp 21–45

  7. Wang HY (2004) Self-Adaptive Neural Network Approaches to Discovering, Visualising and Classifying Patterns in Semi-Structured and Structured Biomedical Data, PhD thesis, University of Ulster

  8. Rezende S, Taborelli R, Félix L, Rocha A (1998) Visualisation for knowledge discovery in databases. In: Proceedings of the International Conference on Data Mining (ICDM’98), Rio de Janeiro, Brasil

  9. Morik K, Wurst M (2002) Knowledge discovery and knowledge visualisation, In: Perspektiven vernetzter Wissensraeume, Workshop, Available at: http://www-ai.cs.uni-dortmund.de/DOKUMENTE/morik_wurst_2002a.pdf

  10. Kohonen T (1995) Self-organising maps. Springer, Heidelberg

    Book  Google Scholar 

  11. Alahakoon D, Halgamuge SK, Srinivasan B (2000) Dynamic self-organising maps with controlled growth for knowledge discovery. IEEE Trans Neural Networks 11(3):601–614

    Article  Google Scholar 

  12. Wang H, Azuaje F, Black N (2004) An integrated and interactive framework for improving biomedical pattern discovery and visualization. IEEE Trans Inform Technol Biomed 8(1):16–27

    Article  Google Scholar 

  13. Fritzke B (1994) Growing cell structure–a self-organising network for unsupervised and supervised learning. Neural Netw 7:1441–1460

    Article  Google Scholar 

  14. Herrero J, Valencia A, Dopazo J (2001) A hierarchical unsupervised growing neural network for clustering gene expression patterns. Bioinformatics 17:126–136

    Article  Google Scholar 

  15. Kim K, Zhang S, Jiang K, Cai L, Lee I, Feldman L, Huang H (2007) Measuring similarities between gene expression profiles through new data transformations. BMC Bioinform 8:29

    Google Scholar 

  16. Zheng H, Wang H, Azuaje F (2008) Improving pattern discovery and visualization of SAGE data through poisson-based self-adaptive neural networks, IEEE Trans Inform Technol Biomed 12(4), pp 459–469

    Google Scholar 

  17. Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E, Lander E, Golub T (1999) Interpreting patterns of gene expression with self-organising maps: methods and application to hematopoietic differentiation. In: Proc Natl Acad Sci USA 96:2907–2912

  18. Fisher RA (1936) The use of multiple measurements in taxonomic problems. Annals of Eugenics VII:179–188

    Google Scholar 

  19. Blackshaw S, Harpavat S, Trimarchi J, Cai L, Huang H, Kuo W, Lee K, Fraioli R, Cho S, Yung R, Asch E, Wong W, Ohno-Machado L, Weber G, Cepko CL (2004) Genomic analysis of mouse retinal development. PLoS Biol 2(9)

  20. Cai L, Huang H, Blackshaw S, Liu JS, Cepko C, Wong W (2004) Clustering analysis of SAGE data: a Poisson approach. Genome Biol 5:R51

    Google Scholar 

  21. Tavazoie S, Hughes JD, Campbell MJ, Cho RJ, Church GM (1999) “Systematic determination of genetic network architecture.” Nat Genet 22:281–285

    Google Scholar 

  22. Reed R (1993) “Pruning algorithms—a survey”. IEEE Trans Neural Netw 4(5):740–747

    Article  Google Scholar 

  23. Velculescu VE, Zhang L, Vogelstein B, Kinzler KW (1997) “Serial analysis of gene expression”. Science 276:1268–1272

    Article  Google Scholar 

  24. Grossberg S (1976) “Adaptive pattern classification and universal recoding: I. parallel development and coding of neural feature detectors”. Biol Cybern 23:121–134

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computing and Mathematics, Computer Science Research Institute, University of Ulster, Shore Road, Newtownabbey, Co. Antrim, BT37 0QB, UK

    Huiru Zheng & Haiying Wang

Authors
  1. Huiru Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haiying Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huiru Zheng.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zheng, H., Wang, H. Improving pattern discovery and visualisation with self-adaptive neural networks through data transformations. Int. J. Mach. Learn. & Cyber. 3, 173–182 (2012). https://doi.org/10.1007/s13042-011-0050-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-011-0050-z

Keywords

Search

Navigation