Mining Association Rules Using Relative Confidence

Do, T. D., Hui, S. C. and Fong, A. C.M. (2004) Mining Association Rules Using Relative Confidence. In: 5th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL04), Exeter, England, 25-27 Aug 2004, pp. 306-313. ISBN 9783540286516 (doi: 10.1007/978-3-540-28651-6_45)

Abstract

Mining for association rules is one of the fundamental tasks of data mining. Association rule mining searches for interesting relationships amongst items for a given dataset based mainly on the support and confidence measures. Support is used for filtering out infrequent rules, while confidence measures the implication relationships from a set of items to one another. However, one of the main drawbacks of the confidence measure is that it presents the absolute value of implication that does not reflect truthfully the relationships amongst items. For example, if two items have a very high frequency, then they will probably form a rule with a high confidence even if there is no relationship between them at all. In this paper, we propose a new measure known as relative confidence for mining association rules, which is able to reflect truthfully the relationships of items. The effectiveness of the relative confidence measure is evaluated in comparison with the confidence measure in mining interesting relationships between terms from textual documents and in associative classification.

• Item Type: Conference Proceedings
• Additional Information: Lecture notes in computer science: 3177
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Fong, Dr Alvis Cheuk Min
• Authors: Do, T. D., Hui, S. C., and Fong, A. C.M.
• College/School: College of Science and Engineering > School of Computing Science
• ISSN: 0302-9743
• ISBN: 9783540286516