Rough association mining and its application in web information gathering

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:
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Year:
2005

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Abstract

It is a big challenge to guarantee the quality of association rules in some application areas (e.g., in information gathering) since duplications and ambiguities of data values (terms). This paper presents a novel concept of rough association rules to improve the quality of discovered knowledge. The precondition 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.