Skip to main content

Using the Normalization for Typographic Errors in Numerals

  • Conference paper
  • 1369 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 6411))

Abstract

For numerical record fields such as date and age, many types of error are likely to yield small numerical differences between observed and true values. If, for example, two different sources provide separate case reports related to the same incident, the dates of onset may not match perfectly but are more likely to differ by a few days than by several years. In order to tackle the variations in numbers a few methods are available. The paper proposes a new normalization technique useful for the numerical record. A Comparison of Distance with the Smith Waterman Distance shows significant increase in the weight by the present technique.

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

Buying options

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

References

  1. Kopal, Z.: Physics and Astronomy of the Moon. Academic Press (1962)

    Google Scholar 

  2. Agrawal, R., Srikant, R.: Searching with numbers. In: Proceedings of the 11th International World Wide Web Conference (WWW11), pp. 420–431 (2002)

    Google Scholar 

  3. Indyk, P., Motwani, R.: Approximate nearest neighbors: Towards removing the curse of dimensionality. In: ACM Symposium on Theory of Computing, pp. 604–613 (1998)

    Google Scholar 

  4. Roussopoulos, N., Kelley, S., Vincent, F.: Nearest neighbor queries. In: Proc. of the 1995 ACM SIGMOD Int’l Conf. on Management of Data, pp. 71–79 (1995)

    Google Scholar 

  5. Crespo, A., Jannink, J., Neuhold, E., Rys, M., Studer, R.: A survey of semi-automatic extraction and transformation, http://www-db.stanford.edu/crespo/publications/

  6. Muslea, I.: Extraction patterns for information extraction tasks: A survey. In: The AAAI 1999 Workshop on Machine Learning for Information Extraction (1999)

    Google Scholar 

  7. Noren, G., Orre, R., Bate, A., Edword, I.: Duplicate detection in adverse drug reaction surveillance. Data Mining and Knowledge Discovery Journal, 306–328 (2007)

    Google Scholar 

  8. http://www.miislita.com/information-retrieval-tutorial/cosine-similarity-tutorial.htmlcosim

  9. Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining, ch. 8, p. 500. Addison-Wesley (2005) ISBN 0-321-32136-7

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Deshmukh, S.N., Mehrotra, S.C., Singh, H. (2012). Using the Normalization for Typographic Errors in Numerals. In: Kannan, R., Andres, F. (eds) Data Engineering and Management. ICDEM 2010. Lecture Notes in Computer Science, vol 6411. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27872-3_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-27872-3_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27871-6

  • Online ISBN: 978-3-642-27872-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics