Application rough sets theory to ordinal scale data for discovering knowledge

Authors:
Shu-Hsien Liao;Yin-Ju Chen;Pei-Hui Chu
Affiliations:
Department of Management Sciences and Decision Making, Tamkang University, Taipei, Taiwan, ROC;Department of Management Sciences, Tamkang University, Taipei, Taiwan, ROC;Department of Information Management, National Taipei College of Business, Taipei, Taiwan, ROC
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Year:
2010

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Abstract

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.