Skip to main content

Trends in Information fusion in Data Mining

  • Chapter
Information Fusion in Data Mining

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 123))

Abstract

This chapter reviews the main uses of information fusion techniques in the field of data mining. A classification of these uses is given into three rough classes: preprocessing, building models and information extraction.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover 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. Bauer, E., Kohavi, R., (1999), An Empiricial Comparison of Voting Classification Algorithms: Bagging, Boosting and Variants, Machine Learning 36 105–139.

    Article  Google Scholar 

  2. Breiman, L., (1996), Bagging predictors, Machine Learning, 24 123–140.

    MathSciNet  MATH  Google Scholar 

  3. Cox, T. F., Cox, M. A. A., (1994), Multidimensional scaling, Chapman and Hall.

    MATH  Google Scholar 

  4. Detyniecki, M., (2000), Mathematical Aggregation Operators and their Application to Video Querying, PhD dissertation, University of Paris VI, Paris, France.

    Google Scholar 

  5. Doyle, P., Lane, J. I., Theeuwes, J. J. M., Zayatz, L. M., (Eds.), (2001), Confidentiality, Disclosure, and Data Access: Theory and Practical Applications for Statistical Agencies, Elsevier.

    Google Scholar 

  6. Godo, L., Torra, V., (2000), On aggregation operators for ordinal qualitative information, IEEE T. on Fuzzy Systems, 8:143–154.

    Article  Google Scholar 

  7. Grabisch, M., (2000), Fuzzy integral for classification and feature extraction, in M. Grabisch, T. Murofushi and M. Sugeno (Eds), Fuzzy Measures and Integrals, Physica-Verlag, 415–434.

    Google Scholar 

  8. Hastie, T., Tibshirani, R., Friedman, J., (2001), The Elements of Statistical Learning, Berlin: Springer.

    MATH  Google Scholar 

  9. Huang, Z., Ng, M. K., (1999), A fuzzy k-modes algorithm for clustering categorical data, IEEE Trans. on Fuzzy Systems, 7:4 446–452.

    Article  Google Scholar 

  10. http://www.integrity.com

    Google Scholar 

  11. Ishibuchi, H., Morisawa, T., Nakashima, T., (1996), Voting Schemes for fuzzyrule-based classification systems, Proc. of the Sixth IEEE Int. Conference on Fuzzy Systems, 614–620, Barcelona, Catalonia, Spain.

    Google Scholar 

  12. Kohonen, T., (1997), Self-Organizing maps, 2nd edition, Springer-Verlag.

    Book  MATH  Google Scholar 

  13. Luo, R.C., Kay, M.G., (1992), Data fusion and sensor integration: State-of-the-art 1990s, in M. Al Abidi, R. C. Gonzalez, (Eds.), Data Fusion in Robotics and Machine Intelligence, Academic Press, 7–135.

    Google Scholar 

  14. Merz, C. J., (1999), Using Correspondence Analysis to Combine Classifiers, Machine Learning, 36 33–58.

    Article  Google Scholar 

  15. Merz, C. J., Pazzani, M. J., (1999), Combining regression estimates, Machine learning, 36 9–32.

    Article  Google Scholar 

  16. Schapire, R. E., (1990), The strength of weak learnability, Machine learning, 5:2 197–227.

    Google Scholar 

  17. Torra, V., (1999), On Some Relationships between Hierarchies of Quasiarithmetic Means and Neural Networks, Int. J. of Intel. Syst. 14:11 1089–1098.

    Article  MATH  Google Scholar 

  18. Torra, V., (2000), Towards the re-identification of individuals in data files with non-common variables, Proc. of the European Conf. on Artificial Intelligence (ECAI 2000), 326–330, Berlin, Germany.

    Google Scholar 

  19. Torra, V., (2003), Information Fusion in Data Mining: Outline, Chapter in this book.

    Google Scholar 

  20. Webb, G.I., (2000), Multi Boosting: A Technique for Combining Boosting and Wagging, Machine Learning, 40 159–196.

    Article  Google Scholar 

  21. Winkler, W. E., (1995), Advanced methods for record linkage, American Statistical Association, Proc. of the Section on Survey Research Methods, 467–472.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Torra, V. (2003). Trends in Information fusion in Data Mining. In: Torra, V. (eds) Information Fusion in Data Mining. Studies in Fuzziness and Soft Computing, vol 123. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36519-8_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-36519-8_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05628-4

  • Online ISBN: 978-3-540-36519-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics