Information Sciences: an International Journal
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
Relational Frequent Patterns Mining for Novelty Detection from Data Streams
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Supporting visual exploration of discovered association rules through multi-dimensional scaling
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Structure learning for belief rule base expert system: A comparative study
Knowledge-Based Systems
Redefinition of Decision Rules Based on the Importance of Elementary Conditions Evaluation
Fundamenta Informaticae
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One of the most important problems with rule induction methods is that it is very difficult for domain experts to check millions of rules generated from large datasets. 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 r1 has reasonable features and the other rule r2 with high similarity to r1 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 similar and dissimilar relations between rules based on multidimensional scaling, which assign a two-dimensional cartesian coordinate to each data point from the information about similiaries 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 could be found.