From rough set theory to evidence theory
Advances in the Dempster-Shafer theory of evidence
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
An Analysis of Quantitative Measures Associated with Rules
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
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One of the most important problems with rule inductionmethods is that it is very difficult for domain experts to check millions of rules generated from large datasets, although the discovery from these rules requires deep interpretation from domain knowledge. Although several solutions have been proposed in the studies on data mining and knowledge discovery, these studies are not focused on similarities between rules obtained. When one rule $r_1$ has reasonable features and the other rule $r_2$ with high similarity to $r_1$ includes unexpected factors, the relations between these rules will become a trigger to the discovery of knowledge. In this paper, we propose a visualization approach to show the similarity relations between rules based on multidimensional scaling, which assign a two-dimensional cartesian coordinate to each data point from the information about similarities between this data and others data. We evaluated this method on two medical data sets, whose experimental results show that knowledge useful for domain experts can be found.