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.
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Bi, Y., Anderson, T., McClean, S. (2003). A Rough Set Model with Ontological Information for Discovering Maximal Association Rules in Document Collections. In: Bramer, M., Preece, A., Coenen, F. (eds) Research and Development in Intelligent Systems XIX. Springer, London. https://doi.org/10.1007/978-1-4471-0651-7_2
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DOI: https://doi.org/10.1007/978-1-4471-0651-7_2
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