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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Application Rough Sets Theory to Ordinal Scale Data for Discovering Knowledge
verfasst von : Shu-Hsien Liao, Yin-Ju Chen, Pei-Hui Chu
Erschienen in: Neural Information Processing. Theory and Algorithms
Verlag: Springer Berlin Heidelberg
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Rough set theory has been applied in many areas such as knowledge discovery and has the ability to deal with incomplete, imprecise or inconsistent information. The traditional association rule which should be fixed in order to avoid both that only trivial rules are retained and also that interesting rules are not discarded. In this paper, the new data mining techniques applied to ordinal scale data, which has the ability to handle the uncertainty in the classing process. The aim of the research is to provide a new association rule concept, which is using ordinal scale data.