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Mining Rough Association from Text Documents

  • Conference paper
Rough Sets and Current Trends in Computing (RSCTC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4259))

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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). This paper presents a novel concept of rough association rules to improve the quality of discovered knowledge in these application areas. 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. It is also feasible to update rough association rules dynamically to produce effective results. The experimental results also 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 Systems and Information Engineering, Maebashi Institute of Technology, Japan

    Ning Zhong

Authors
  1. Yuefeng Li
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  2. Ning Zhong
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Editor information

Editors and Affiliations

  1. Faculty of Economics, University of Catania, Corso Italia, 55, 95129, Catania, Italy

    Salvatore Greco

  2. Graduate School of Engineering, Department of Electrical Engineering and Computer Sciences, University of Hyogo, 2167 Shosha, 671-2280,, Himeji, Hyogo, Japan

    Yutaka Hata

  3. Department of Medical Informatics, Faculty of Medicine, Shimane University, 89-1 Enya-cho, Izumo, 693-8501, Shimane, Japan

    Shoji Hirano

  4. Department of Systems Innovation, Graduate School of Engineering Science, Osaka University, 1-3, Machikaneyama, Toyonaka, 560-8531, Osaka, Japan

    Masahiro Inuiguchi

  5. Department of Risk Engineering, School of Systems and Information Engineering, University of Tsukuba, 305-8573, Ibaraki, Japan

    Sadaaki Miyamoto

  6. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Hung Son Nguyen

  7. Systems Research Institute, Polish Academy of Sciences, 01-447, Warsaw, Poland

    Roman Słowiński

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© 2006 Springer-Verlag Berlin Heidelberg

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Li, Y., Zhong, N. (2006). Mining Rough Association from Text Documents. In: Greco, S., et al. Rough Sets and Current Trends in Computing. RSCTC 2006. Lecture Notes in Computer Science(), vol 4259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11908029_39

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  • DOI: https://doi.org/10.1007/11908029_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-47693-1

  • Online ISBN: 978-3-540-49842-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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