Skip to main content
SpringerLink
Log in

Data mining using rule extraction from Kohonen self-organising maps

  • Original Article
  • Published:
Neural Computing & Applications Aims and scope Submit manuscript

Abstract

The Kohonen self-organising feature map (SOM) has several important properties that can be used within the data mining/knowledge discovery and exploratory data analysis process. A key characteristic of the SOM is its topology preserving ability to map a multi-dimensional input into a two-dimensional form. This feature is used for classification and clustering of data. However, a great deal of effort is still required to interpret the cluster boundaries. In this paper we present a technique which can be used to extract propositional IF..THEN type rules from the SOM network’s internal parameters. Such extracted rules can provide a human understandable description of the discovered clusters.

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

Access this article

Log in via an institution

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
Fig. 10

Similar content being viewed by others

References

  1. Andrews R, Diederich J, Tickle A (1998) The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural networks. IEEE Trans Neural Netw 9(6):1057–1068

    Article  Google Scholar 

  2. Bahamonde A (1997) Self-organizing symbolic learned rules. In: Proceedings of the international conference on artificial and natural neural networks, Lanazrote, Canaries

  3. Bengio Y, Buhmann J, Embrechts M, Zurada J (2000) Introduction to the special issue on neural networks for data mining and knowledge discovery. IEEE Trans Neural Netw 11(3):545–547

    Article  Google Scholar 

  4. Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery: an overview. In: Fayyad U, Piatetsky-Shapiro G, Smyth P, Uthursamy R (eds) Advances in knowledge discovery and data mining. AAAI-Press, pp 1–34

  5. Freitas A (1998) On objective measures of rule suprisingness. In: Principles of data mining and knowledge discovery: proceedings of the 2nd European symposium, lecture notes in artificial intelligence, vol 1510. Nantes, France, pp 1–9

  6. Freitas A (1999) On rule interestingness measures. Knowl Based Syst 12(5–6):309–315

    Article  Google Scholar 

  7. Fritzke B (1996) Growing self-organizing networks---why? In: Proceedings of the European symposium on artificial neural networks, Brussels, pp 61--72

  8. Holte RC, Acker LE, Porter BW (1989) Concept learning and the problem of small disjuncts. In: Proceedings of the 11th international joint conference on artificial intelligence, Detroit, pp 813–818

  9. Hong ZQ, Yang JY (1991) Optimal discriminant plane for a small number of samples and design method of classifier on the plane. Pattern Recognit 24(4):317–324

    Article  Google Scholar 

  10. Koh J, Suk M, Bhandarkar SM (1995) A multilayer self-organizing feature map for range image segmentation. Neural Netw 8(1):67–86

    Article  Google Scholar 

  11. Kohonen T, Oja E, Simula O, Visa A, Kangas J (1996) Engineering applications of the self-organizing map. Proc IEEE 84(10):1358–1383

    Article  Google Scholar 

  12. Li X, Gasteiger J, Zupan J (1993) On the topology distortion in self-organizing feature maps. Biol Cybern 70:189–198

    Article  Google Scholar 

  13. Liu B, Hsu W, Mun L, Lee HY (1999) Finding interesting patterns using user expectations. IEEE Trans Knowl Data Eng 11(6):817–832

    Article  Google Scholar 

  14. McGarry K (2002) The analysis of rules discovered by the data mining process. In: Proceedings of 4th international conference on recent advances in soft computing, Nottingham, UK, pp 255–260

  15. McGarry K, Wermter S, MacIntyre J (2001) The extraction and comparison of knowledge from local function networks. Int J Comput Intell Appl 1(4):369–382

    Article  Google Scholar 

  16. McGarry K, Wermter S, MacIntyre J (2001) Knowledge extraction from local function networks. In: Seventeenth international joint conference on artificial intelligence, vol 2. Seattle, USA, pp 765–770,

  17. Mitra S, Hayashi Y (2000) Neuro-fuzzy rule generation: survey in soft computing framework. IEEE Trans Neural Netw 11(3):748–768

    Article  Google Scholar 

  18. Mitra S, Pal S (2002) Data mining in soft computing framework: a survey. IEEE Trans Neural Netw 13(1):314

    Article  Google Scholar 

  19. Ono K (1998) New approaches for data mining in corporate environments. In: Proceedings of the 2nd international conference on the practical application of knowledge discovery and data mining, London, UK, pp 49–65

  20. Piatetsky-Shapiro G, Matheus CJ (1994) The interestingness of deviations. In: Proceedings of AAAI workshop on knowledge discovery in databases

  21. Rubio M, Gimenez V (2003) New methods for self-organising map visual analysis. Neural Comput Appl 12(3–4):142–152

    Article  Google Scholar 

  22. Sharpe PK, Caleb P (1998) Self organising maps for the investigation of clinical data: a case study. Neural Comput Appl 7:65–70

    Google Scholar 

  23. Silberschatz A, Tuzhilin T (1995) On subjective measures of interestingness in knowledge discovery. In: Proceedings of the 1st international conference on knowledge discovery and data mining, pp 275–281

  24. Ultsch A, Korus D (1995) Automatic acquisition of symbolic knowledge from subsymbolic neural nets. In: Proceedings of the 3rd European conference on intelligent techniques and soft computing, pp 326–331

  25. Ultsch A, Mantyk R, Halmans G (1993) Connectionist knowledge acquisition tool: CONKAT. In: Hand J (ed) Artificial intelligence frontiers in statistics: AI and statistics III. Chapman and Hall, pp 256–263

  26. Vesanto J, Alhonemi E (2000) Clustering of the self-organizing map. IEEE Trans Neural Netw 11(3):586–600

    Article  Google Scholar 

  27. Villmann T (1998) Estimation of topology in self-organizing maps and data driven growing of suitable network structures. In: Proceedings of the 6th European conference on intelligent techniques and soft computing. Aachen, Germany, pp 235–244

  28. Watkins D (1998) Discovering geographical clusters in a US telecommunications company call detail records using Kohonen self organising maps. In: Proceedings of the 2nd international conference on the practical application of knowledge discovery and data mining, London, UK, pp 67–73

  29. Wermter S, Sun R (2000) Hybrid neural systems. Springer, Berlin Heidelberg New York

    Google Scholar 

  30. Witkowski U, Ruping S, Rucket U, Schutte F, Beineke S, Grotstollen H (1997) System identification using self-organizing feature maps. In: IEE, proceedings of the 5th international conference on artificial neural networks, Cambridge, England, pp 100–105

  31. Zhang CX, Mlynski DA (1997) Mapping and hierarchical self-organizing neural networks for VLSI placement. IEEE Trans Neural Netw 8(2):299–314

    Article  Google Scholar 

Download references

Acknowledgments

We would like to thank the researchers at Helsinki University of Technology for providing their SOM model [http://www.cis.hut.−/projects/somtoolbox/], EPSRC (grant GR/P01205) and Nonlinear Dynamics.

Author information

Authors and Affiliations

  1. School of Computing and Technology, University of Sunderland, St Peters Campus, St Peters Way, Sunderland, SR6 ODD, UK

    James Malone, Kenneth McGarry, Stefan Wermter & Chris Bowerman

Authors
  1. James Malone
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kenneth McGarry
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stefan Wermter
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chris Bowerman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to James Malone.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Malone, J., McGarry, K., Wermter, S. et al. Data mining using rule extraction from Kohonen self-organising maps. Neural Comput & Applic 15, 9–17 (2006). https://doi.org/10.1007/s00521-005-0002-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-005-0002-1

Keywords

Search

Navigation