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Knowledge Discovery in Textual Databases: A Concept-Association Mining Approach

In: Data Engineering

Author

Listed:
  • Mutlu Mete

    (Texas A&M University-commerce)

  • Nurcan Yuruk

    (University of Arkansas at Little Rock)

  • Xiaowei Xu

    (University of Arkansas at Little Rock)

  • Daniel Berleant

    (University of Arkansas at Little Rock)

Abstract

The number of scientific publications is exploding as online digital libraries and the World Wide Web grow. MEDLINE, the premier bibliographic database of the National Library of Medicine (NLM)National Library of Medicine (NLM) , contains about 18 million records from more than 7,300 different publications dating from 1965; it is growing by about 400,000 citations each year. The explosive growth of information in textual documents creates great need for techniques for knowledge discovery from text collections.

Suggested Citation

  • Mutlu Mete & Nurcan Yuruk & Xiaowei Xu & Daniel Berleant, 2009. "Knowledge Discovery in Textual Databases: A Concept-Association Mining Approach," International Series in Operations Research & Management Science, in: Yupo Chan & John Talburt & Terry M. Talley (ed.), Data Engineering, chapter 11, pages 225-243, Springer.
  • Handle: RePEc:spr:isochp:978-1-4419-0176-7_11
    DOI: 10.1007/978-1-4419-0176-7_11
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    Cited by:

    1. Xu Chen & Chunhong Liu & Yao Jiang & Changchun Gao, 2021. "What Causes the Virtual Agglomeration of Creative Industries?," Sustainability, MDPI, vol. 13(16), pages 1-18, August.

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