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A Rough Set Model with Ontological Information for Discovering Maximal Association Rules in Document Collections

Bi, Yaxin, Anderson, T. and McClean, S. (2003) A Rough Set Model with Ontological Information for Discovering Maximal Association Rules in Document Collections. In: Research and Development in Intelligent Systems XIX. Springer London, pp. 19-32. ISBN 978-1-85233-674-5 [Book section]

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Abstract

In this paper we investigate the applicability of a Rough Set model and method to discover maximal associations from a collection of text documents, and compare its applicability with that of the maximal association method. Both methods are based on computing co-occurrences of various sets of keywords, but it has been shown that by using the Rough Set method, rules discovered are similar to maximal association rules, and it is much simpler than the maximal association method. In addition, we also present an alternative strategy to taxonomies required in the above methods, instead of building taxonomies based on labelled document collections themselves. This is to effectively utilise ontologies which will increasingly be deployed on the Internet.

Item Type:Book section
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:25639
Deposited By: Dr Yaxin Bi
Deposited On:22 Jan 2015 10:31
Last Modified:22 Jan 2015 10:31

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