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Reducts in Incomplete Decision Tables

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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

Knowledge reduction is an important issue in data mining. This paper focuses on the problem of knowledge reduction in incomplete decision tables. Based on a concept of incomplete conditional entropy, a new reduct definition is presented for incomplete decision tables and its properties are analyzed. Compared with several existing reduct definitions, the new definition has a better explanation for knowledge uncertainty and is more convenient for application of the idea of approximate reduct in incomplete decision tables.

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Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Yantai Normal University, Yantai, 264025, China

    Renpu Li

  2. College of Information, East China University of Science and Technology, Shanghai, 200237, China

    Renpu Li & Dao Huang

Authors
  1. Renpu Li
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  2. Dao Huang
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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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Li, R., Huang, D. (2005). Reducts in Incomplete Decision Tables. 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_20

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  • DOI: https://doi.org/10.1007/11527503_20

  • 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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