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A Comprehensively Sized Decision Tree Generation Method for Interactive Data Mining of Very Large Databases

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Advanced Data Mining and Applications (ADMA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3584))

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Abstract

For interactive data mining of very large databases a method working with relatively small training data that can be extracted from the target databases by sampling is proposed, because it takes very long time to generate decision trees for the data mining of very large databases that contain many continues data values, and size of decision trees has the tendency of dependency on the size of training data. The method proposes to use samples of confidence in proper size as the training data to generate comprehensible trees as well as to save time. For medium or small databases direct use of original data with some harsh pruning may be used, because the pruning generates trees of similar size with smaller error rates.

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Author information

Authors and Affiliations

  1. Division of Internet Engineering, Dongeo University, Busan, 617-716, South Korea

    Hyontai Sug

Authors
  1. Hyontai Sug
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Editor information

Editors and Affiliations

  1. School of Information Technology and Electrical Engineering, The University of Queensland, 4072, Brisbane, Queensland, Australia

    Xue Li

  2. The State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 430072, Wuhan, China

    Shuliang Wang

  3. School of ITEE, The Univ of Queensland, St. Lucia, 4072, QLD, Australia

    Zhao Yang Dong

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© 2005 Springer-Verlag Berlin Heidelberg

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Sug, H. (2005). A Comprehensively Sized Decision Tree Generation Method for Interactive Data Mining of Very Large Databases. In: Li, X., Wang, S., Dong, Z.Y. (eds) Advanced Data Mining and Applications. ADMA 2005. Lecture Notes in Computer Science(), vol 3584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527503_17

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  • DOI: https://doi.org/10.1007/11527503_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-27894-8

  • Online ISBN: 978-3-540-31877-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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