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Transactions on Rough Sets VII pp 103–119Cite as

Mining Rough Association from Text Documents for Web Information Gathering

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Part of the book series: Lecture Notes in Computer Science ((TRS,volume 4400))

Abstract

It is a big challenge to guarantee the quality of association rules in some application areas (e.g., in Web information gathering) since duplications and ambiguities of data values (e.g., terms). Rough set based decision tables could be efficient tools for solving this challenge. This paper first illustrates the relationship between decision tables and association mining. It proves that a decision rule is a kind of closed pattern. It also presents an alternative concept of rough association rules to improve the quality of discovered knowledge in this area. The premise of a rough association rule consists of a set of terms (items) and a weight distribution of terms (items). The distinct advantage of rough association rules is that they contain more specific information than normal association rules. This paper also conducts some experiments to compare the proposed method with association rule mining and decision tables; and the experimental results verify that the proposed approach is promising.

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Author information

Authors and Affiliations

  1. School of Software Engineering and Data Communications, Queensland University of Technology, Brisbane QLD 4001, Australia

    Yuefeng Li

  2. Department of Information Engineering, Maebashi Institute of Technology, Maebashi 371-0816, Japan

    Ning Zhong

Authors
  1. Yuefeng Li
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  2. Ning Zhong
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Editor information

James F. Peters Andrzej Skowron Victor W. Marek Ewa Orłowska Roman Słowiński Wojciech Ziarko

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Li, Y., Zhong, N. (2007). Mining Rough Association from Text Documents for Web Information Gathering. In: Peters, J.F., Skowron, A., Marek, V.W., Orłowska, E., Słowiński, R., Ziarko, W. (eds) Transactions on Rough Sets VII. Lecture Notes in Computer Science, vol 4400. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71663-1_7

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  • DOI: https://doi.org/10.1007/978-3-540-71663-1_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71662-4

  • Online ISBN: 978-3-540-71663-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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