Skip to main content
SpringerLink
Log in
Book cover

International Conference on Artificial Intelligence and Soft Computing

ICAISC 2012: Artificial Intelligence and Soft Computing pp 181–187Cite as

An Application of the Self-Organizing Map to Multiple View Unsupervised Learning

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7268))

Abstract

In various data mining applications performing the task of extracting information from large databases is serious problem, which occurs in many fields e.g.: bioinformatics, commercial behaviour of Internet users, social networks analysis, management and investigation of various databases in static or dynamic states. In recent years many techniques discovering hidden structures in the data set like clustering and projection of data from high-dimensional spaces have been developed. In this paper, we propose a model for multiple view unsupervised clustering based on Kohonen self-organizing-map algorithm. The results of simulations in two dimensional space using three views of training sets having different statistical properties have been presented.

This is a preview of subscription content, 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   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Berkhin, P.: Survey of clustering data mining techniques. Technical report, Accrue Software, San Jose, CA (2002)

    Google Scholar 

  2. Bickel, S., Scheffer, T.: Multi-view clustering. In: ICDM 2004, pp. 19–26 (2004)

    Google Scholar 

  3. Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: COLT 1998, pp. 92–100 (1998)

    Google Scholar 

  4. Long, B., Yu, P.S., Zhang, Z.: A General Model for Multiple View Unsupervised Learning. In: Proceedings of the 2008 SIAM International Conference on Data Mining, pp. 822–833 (2008)

    Google Scholar 

  5. Brefeld, U., Scheffer, T.: Co-em support vector learning. In: ICML 2004, p. 16 (2004)

    Google Scholar 

  6. Cierniak, R., Rutkowski, L.: On image compression by competitive neural networks and optimal linear predictors. Signal Processing: Image Communication - a Eurasip Journal 15(6), 559–565 (2000)

    Article  Google Scholar 

  7. Collins, M., Singer, Y.: Unsupervised models for named entity classification (1999)

    Google Scholar 

  8. Fern, X.Z., Brodley, C.E.: Solving cluster ensemble problems by bipartite graph partitioning. In: ICML 2004 (2004)

    Google Scholar 

  9. Ghani, R.: Combining labeled and unlabeled data for text classification with a large number of categories. In: ICDM 2001, pp. 597–598 (2001)

    Google Scholar 

  10. Hedar, A.R., Wang, J., Fukushima, M.: Tabu search for attribute reduction in rough set theory. Soft Computing 12, 909–918 (2008)

    Article  MATH  Google Scholar 

  11. Herawan, T., Ghazali, R., Deris, M.M.: Soft Set Theoretic Approach for Dimensionality Reduction. International Journal of Database Theory and Application 3(2) (June 2010)

    Google Scholar 

  12. Jain, A.K., Murty, M.N., Flynn, P.J.: Data cluster-ing: a review. ACM Computing Surveys 31(3), 264–323 (1999)

    Article  Google Scholar 

  13. Karypis, G., Kumar, V.: A fast and high quality mul-tilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comput. 20(1), 359–392 (1998)

    Article  MathSciNet  Google Scholar 

  14. Kohonen, T.: The Self-Organizing Map. Proceedings of The IEEE 78(9) (September 1990)

    Google Scholar 

  15. Long, B., Yu, P.S., Zhang, Z.: A General Model for Multiple View Unsupervised Learning. In: Proceedings of the 2008 SIAM International Conference on Data Mining, pp. 822–833 (2008)

    Google Scholar 

  16. Nigam, K., Ghani, R.: Analyzing the effectiveness and applicability of co-training. In: CIKM 2000, pp. 86-93 (2000)

    Google Scholar 

  17. Sa, V.R.: Spectral clustering with two views. In: ICML Workshop on Learning with Multiple Views (2005)

    Google Scholar 

  18. Taskar, B., Segal, E., Koller, D.: Probabilistic classification and clustering in relational data. In: Proceeding of IJCAI 2001 (2001)

    Google Scholar 

  19. Topchy, A., Jain, A.K., Punch, W.: Combining multiple weak clusterings. In: Proceedings of the Third IEEE International Conference on Data Mining, p. 331 (2003)

    Google Scholar 

  20. Wang, Y., Gongxuan, Z., Qian, J.-B.: ApproxCCA: An approximate correlation analysis algorithm for multidimensional data streams. Knowledge-Based Systems 24, 952–962 (2011)

    Article  Google Scholar 

  21. Chen, Y., Yao, Y.: A multiview approach for intelligent data analysis based on data operators. Information Sciences 178, 1–20 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  22. Zhou, D., Burge, C.J.C.: Spectral clustering and transductive learning with multiple views. In: ICML 2007, pp. 1159–1166 (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Częstochowa University of Technology, Al. Armii Krajowej 36, 42-200, Częstochowa, Poland

    Tomasz Gałkowski & Artur Starczewski

Authors
  1. Tomasz Gałkowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Artur Starczewski
    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 , Marcin Korytkowski  & Rafał Scherer ,  & 

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

    Ryszard Tadeusiewicz

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

    Lotfi A. Zadeh

  4. 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

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Gałkowski, T., Starczewski, A. (2012). An Application of the Self-Organizing Map to Multiple View Unsupervised Learning. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7268. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29350-4_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-29350-4_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29349-8

  • Online ISBN: 978-3-642-29350-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation