Advertisement

Combining Two Data Mining Methods for System Identification

  • Sandro Saitta
  • Benny Raphael
  • Ian F. C. Smith
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4200)

Abstract

System identification is an abductive task which is affected by several kinds of modeling assumptions and measurement errors. Therefore, instead of optimizing values of parameters within one behavior model, system identification is supported by multi-model reasoning strategies. The objective of this work is to develop a data mining algorithm that combines principal component analysis and k-means to obtain better understandings of spaces of candidate models. One goal is to improve views of model-space topologies. The presence of clusters of models having the same characteristics, thereby defining model classes, is an example of useful topological information. Distance metrics add knowledge related to cluster dissimilarity. Engineers are thus better able to improve decision making for system identification and downstream tasks such as further measurement, preventative maintenance and structural replacement.

Keywords

Data Mining Score Function Data Mining Technique Data Mining Algorithm Data Mining Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alonso, C., Rodriguez, J.J., Pulido, B.: Enhancing Consistency based Diagnosis with Machine Learning Techniques. LNCS, vol. 3040, pp. 312–321 (2004)Google Scholar
  2. 2.
    Chan, Z.S.H., Collins, L., Kasabov, N.: An efficient greedy k-means algorithm for global gene trajectory clustering. Exp. Sys. with Appl. 30(1), 137–141 (2006)CrossRefGoogle Scholar
  3. 3.
    Ding, C., He, X.: K-means clustering via principal component analysis. In: Proceedings of the 21st International Conference on Machine Learning (2004)Google Scholar
  4. 4.
    Hand, D., Mannila, H., Smyth, P.: Principles of Data Mining, p. 546. MIT Press, Cambridge (2001)Google Scholar
  5. 5.
    Jolliffe, I.T.: Principal Component Analysis. Statistics Series, p. 271. Springer, Heidelberg (1986)Google Scholar
  6. 6.
    Ljung, L.: System Identification - Theory For the User, p. 609. Prentice-Hall, Englewood Cliffs (1999)Google Scholar
  7. 7.
    Melhem, H.G., Cheng, Y.: Prediction of Remaining Service Life of Bridge Decks Using Machine Learning. J. Comp. in Civ. Eng. 17(1), 1–9 (2003)CrossRefGoogle Scholar
  8. 8.
    Nguyen, H.H., Chan, C.W.: Applications of data analysis techniques for oil production prediction. Art. Int. in Eng. 13, 257–272 (1999)CrossRefGoogle Scholar
  9. 9.
    Ordonez, C.: Integrating k-means clustering with a relational DBMS using SQL. IEEE Trans. on Know. and Data Eng. 18(2), 188–201 (2006)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Pan, X., Ye, X., Zhang, S.: A hybrid method for robust car plate character recognition. Eng. Appl. of Art. Int. 18(8), 963–972 (2005)CrossRefGoogle Scholar
  11. 11.
    Picone, J.: Duration in context clustering for speech recognition. Speech Com. 9(2), 119–128 (1990)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Raphael, B., Smith, I.F.C.: Fundamentals of Computer-Aided Engineering, p. 306. John Wiley, Chichester (2003)Google Scholar
  13. 13.
    Reich, Y., Barai, S.V.: Evaluating machine learning models for engineering problems. Art. Int. in Eng. 13, 257–272 (1999)CrossRefGoogle Scholar
  14. 14.
    Robert-Nicoud, Y., Raphael, B., Smith, I.F.C.: Improving the reliability of system identification. Next Gen. Int. Sys. in Eng. 199, 100–109 (2004)Google Scholar
  15. 15.
    Saitta, S., Raphael, B., Smith, I.F.C.: Data mining techniques for improving the reliability of system identification. Adv. Eng. Inf. 19(4), 289–298 (2005)CrossRefGoogle Scholar
  16. 16.
    Shirazi Kia, S., Noroozi, S., Carse, B., Vinney, J.: Application of Data Mining Techniques in Predicting the Behaviour of Composite Joints. In: Eighth AICC, Paper 18 (2005) (CD-ROM)Google Scholar
  17. 17.
    Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining, p. 769. Addison-Wesley, Reading (2006)Google Scholar
  18. 18.
    Webb, A.: Statistical Pattern Recognition, p. 496. Wiley, Chichester (2002)MATHCrossRefGoogle Scholar
  19. 19.
    Xu, L.J., Yan, Y., Cornwell, S., Riley, G.: Online fuel tracking by combining principal component analysis and neural network techniques. IEEE Trans. on Inst. and Meas. 54(4), 1640–1645 (2005)CrossRefGoogle Scholar
  20. 20.
    Yan, L., Fraser, M., Oliver, K., Elgamal, A., Conte, J.P., Fountain, T.: Traffic Pattern Recognition using an Active Learning Neural Network and Principal Components Analysis. In: Eighth AICC, Paper 48 (2005) (CD-ROM)Google Scholar
  21. 21.
    Yun, C.-B., Yi, J.-H., Bahng, E.Y.: Joint damage assessment of framed structures using a neural networks technique. Eng. Struct. 23, 425–435 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sandro Saitta
    • 1
  • Benny Raphael
    • 2
  • Ian F. C. Smith
    • 1
  1. 1.IMACEcole Polytechnique Fédérale de LausanneLausanneSwitzerland
  2. 2.Department of BuildingNational University of SingaporeSingapore

Personalised recommendations