NDLI: Visualization of rule's similarity using multidimensional scaling

Content Provider	IEEE Xplore Digital Library
Author	Tsumoto, S. Hirano, S.
Copyright Year	2003
Description	Author affiliation: Dept. of Med. Informatics, Shimane Univ., Japan (Tsumoto, S.; Hirano, S.)
Abstract	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 r/sub 1/ has reasonable features and the other rule r/sub 2/ with high similarity to r/sub 1/ includes unexpected factors, the relations between these rules will become a trigger to the discovery of knowledge. We propose a visualization approach to show the similar 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 could be found.
Sponsorship	IEEE Comput. Soc. Tech. Committee on Computational Intelligence IEEE Comput. Soc. Tech. Committee on Pattern Analysis and Machine Intelligence
Starting Page	339
Ending Page	346
File Size	371147
Page Count	8
File Format	PDF
ISBN	0769519784
DOI	10.1109/ICDM.2003.1250938
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-11-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multidimensional systems Microorganisms Liver diseases Diabetes Bone diseases Induction generators Data mining Data visualization Biomedical informatics Cities and towns
Content Type	Text
Resource Type	Article

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