Mining rough association from text documents

  • Authors:
  • Yuefeng Li;Ning Zhong

  • Affiliations:
  • School of Software Engineering and Data Communications, Queensland University of Technology, Brisbane, QLD, Australia;Department of Systems and Information Engineering, Maebashi Institute of Technology, Japan

  • Venue:
  • RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
  • Year:
  • 2006

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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.