Scientific discovery: computational explorations of the creative process
Scientific discovery: computational explorations of the creative process
From rough set theory to evidence theory
Advances in the Dempster-Shafer theory of evidence
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Information Sciences: an International Journal
Mining Ontology for Automatically Acquiring Web User Information Needs
IEEE Transactions on Knowledge and Data Engineering
Conceptual equivalence for contrast mining in classification learning
Data & Knowledge Engineering
International Journal of Intelligent Information and Database Systems
New similarity rules for mining data
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
Interactive comprehensible data mining
Ambient Intelligence for Scientific Discovery
A stability-considered density-adaptive routing protocol in MANETs
Journal of Systems Architecture: the EUROMICRO Journal
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One of the most important problems with rule inductionmethods is that it is very difficult for domain experts to checkmillions of rules generated from large datasets. The discoveryfrom these rules requires deep interpretation from domainknowledge. Although several solutions have been proposedin the studies on data mining and knowledge discovery,these studies are not focused on similarities betweenrules obtained. When one rule r1 has reasonable featuresand the other rule r2 with high similarity to r1 includes unexpectedfactors, the relations between these rules will becomea trigger to the discovery of knowledge. In this paper,we propose a visualization approach to show the similarrelations between rules based on multidimensional scaling,which assign a two-dimensional cartesian coordinateto each data point from the information about similiariesbetween this data and others data. We evaluated this methodon two medical data sets, whose experimental results showthat knowledge useful for domain experts could be found.