Skip to main content
SpringerLink
Log in

Essential Attributes Generation for Some Data Mining Tasks

  • Conference paper
Artificial Intelligence and Soft Computing (ICAISC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8468))

Included in the following conference series:

  • 2153 Accesses

Abstract

In this paper, we introduce a new approach referred to as Essential Attributes Generation (EAG) to reduce the dimensionality of multidimensional real-valued data series. We form a new representation of the original data. The approach is based on the concept of essential attributes generated by a multilayer neural network. The EAG generates a vector of real valued new attributes which form the compressed representation of the original data. The attributes are synthetic, and while not being directly interpretable, they still retain important features of the original data series. The approach has found applications to classification as well as clustering tasks.

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

Access this chapter

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Astrom, K.J.: On the choice of sampling rates in parametric identification of time series. Information Sciences 1(3), 273–278 (1969)

    Article  MathSciNet  Google Scholar 

  2. Azzouzi, M., Nabney, I.T.: Analyzing time series structure with Hidden Markov Models. In: Proceedings of the IEEE Conference on Neural Networks and Signal Processing, pp. 402–408 (1998)

    Google Scholar 

  3. Chan, K.P., Fu, A.C.: Efficient time series matching by wavelets. In: Proceedings of the 15th IEEE International Conference on Data Engineering, pp. 126–133 (1999)

    Google Scholar 

  4. Cybenko, G.: Approximations by superpositions of sigmoidal functions. Mathematics of Control, Signals, and Systems 2(4), 303–314 (1989)

    Article  MATH  MathSciNet  Google Scholar 

  5. Dreyfus, G.: Neural Networks Methodology and Applications. Springer, Berlin (2005)

    MATH  Google Scholar 

  6. Faloutsos, C., Ranganathan, M., Manolopulos, Y.: Fast subsequence matching in time-series databases. SIGMOD Record 23, 519–529 (1994)

    Article  Google Scholar 

  7. Frohlich, H., Chapelle, O., Scholkopf, B.: Feature selection for support vector machines by means of genetic algorithms. In: ICTAI, pp. 142–148 (2003)

    Google Scholar 

  8. Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L. (eds.): Feature extraction foundations and applications. Springer, Berlin (2005)

    Google Scholar 

  9. Hall, M., Holmes, G.: Benchmarking attribute selection techniques for discrete class data mining. IEEE Trans. Knowl. Data Eng. 15(6), 1437–1447 (2003)

    Article  Google Scholar 

  10. Inselberg, A.: Parallel Coordinates: VISUAL Multidimensional Geometry and its Applications. Springer (2009)

    Google Scholar 

  11. Jolliffe, I.T.: Principal Component Analysis. Springer, Berlin (2002)

    MATH  Google Scholar 

  12. Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Dimensionality reduction for fast similarity search in large time series databases. J. Knowl. Inform. Syst. 3(3), 263–286 (2000)

    Article  Google Scholar 

  13. Krawczak, M.: Multilayer Neural Systems and Generalized Net Models. Ac. Publ. House EXIT, Warsaw (2003a)

    Google Scholar 

  14. Krawczak, M.: Heuristic dynamic programming - Learning as control problem. In: Rutkowski, L., Kacprzyk, J. (eds.) Neural Networks and Soft Computing, pp. 218–223. Physica Verlag, Heidelberg (2003b)

    Chapter  Google Scholar 

  15. Krawczak, M., Szkatuła, G.: Time series envelopes for classification. In: IEEE Intelligent Systems Conference, London, July 7-9 (2010)

    Google Scholar 

  16. Krawczak, M., Szkatuła, G.: A hybrid approach for dimension reduction in classification. Control and Cybernetics 40(2), 527–552 (2011)

    Google Scholar 

  17. Krawczak, M., Szkatuła, G.: Nominal Time Series Representation for the Clustering Problem. In: IEEE 6th International Conference, Intelligent Systems, Sofia, pp. 182–187 (2012)

    Google Scholar 

  18. Krawczak, M., Szkatuła, G.: An approach to dimensionality reduction in time series. Information Sciences 260, 15–36 (2014)

    Article  MathSciNet  Google Scholar 

  19. Lin, J., Keogh, E., Wei, L., Lonardi, S.: Experiencing SAX: a novel symbolic representation of time series. Journal Data Mining and Knowledge Discovery 15(2), 107–144 (2007)

    Article  MathSciNet  Google Scholar 

  20. Lee, S., Kwon, D., Lee, S.: Dimensionality reduction for indexing time series based on the minimum distance. Journal of Inform. Science and Engineering 19, 697–711 (2003)

    MathSciNet  Google Scholar 

  21. Maimon, O., Rokach, L. (eds.): Data mining and knowledge discovery handbook. Springer (2010)

    Google Scholar 

  22. Fu, T.-C.: A review on time series data mining. Engineering Applications of Artificial Intelligence 24, 164–181 (2011)

    Article  Google Scholar 

  23. Yang, K., Shahabi, C.: On the stationarity of multivariate time series for correlation-based data analysis. In: Proceedings of the Fifth IEEE International Conference on Data Mining, pp. 805–808 (2005)

    Google Scholar 

  24. Yi, B.K., Faloutsos, C.: Fast time sequence indexing for arbitrary norms. In: Proceedings of International Conference on Very Large Data Bases, Cairo, Egypt (2000)

    Google Scholar 

  25. Wnek, J., Michalski, R.S.: Hypothesis-driven Constructive Induction in AQ17-HCI: A Method and Experiments. Machine Learning 14, 139–168 (1994)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447, Warsaw, Poland

    Maciej Krawczak & Grażyna Szkatuła

  2. Warsaw School of Information Technology, Newelska 6, 01-447, Warsaw, Poland

    Maciej Krawczak

Authors
  1. Maciej Krawczak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Grażyna Szkatuła
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski  & Rafał Scherer  & 

  2. Częstochowa University of Technology, 42-200, Częstochowa, Poland

    Marcin Korytkowski

  3. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  4. Computer Science Division, Department of Electrical Engineering and Computer Sciences, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  5. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Krawczak, M., Szkatuła, G. (2014). Essential Attributes Generation for Some Data Mining Tasks. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2014. Lecture Notes in Computer Science(), vol 8468. Springer, Cham. https://doi.org/10.1007/978-3-319-07176-3_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-07176-3_5

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07175-6

  • Online ISBN: 978-3-319-07176-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation