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Knowledge Visualization Using Optimized General Logic Diagrams

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dc.contributor.author Śnieżyński, Bartłomiej
dc.contributor.author Szymacha, Robert
dc.contributor.author Michalski, Ryszard S.
dc.date.accessioned 2006-11-03T18:17:31Z
dc.date.available 2006-11-03T18:17:31Z
dc.date.issued 2005-06 en_US
dc.identifier.citation Śnieżyński, B., Szymacha, R. and Michalski, R. S., "Knowledge Visualization Using Optimized General Logic Diagrams," Proceedings of the Intelligent Information Processing and Web Mining Conference, IIPWM 05, Gdansk, Poland, June 13-16, 2005. en_US
dc.identifier.uri https://hdl.handle.net/1920/1492
dc.description.abstract Knowledge Visualizer (KV) uses a General Logic Diagram (GLD) to display examples and/or various forms of knowledge learned from them in a planar model of a multi-dimensional discrete space. Knowledge can be in different forms, for example, decision rules, decision trees, logical expressions, clusters, classifiers, and neural nets with discrete input variables. KV is implemented as a module of the inductive database system VINLEN, which integrates a conventional database system with a range of inductive inference and data mining capabilities. This paper describes briefly the KV module and then focuses on the problem of arranging attributes that span the diagram in a way that leads to the most readable rule visualization in the diagram. This problem has been solved by applying a simulated annealing.
dc.format.extent 2148 bytes
dc.format.extent 1523293 bytes
dc.format.mimetype text/xml
dc.format.mimetype application/pdf
dc.language.iso en_US en_US
dc.relation.ispartofseries P 05-3 en_US
dc.subject knowledge visualization en_US
dc.subject general logic diagrams (GLD) en_US
dc.subject diagram optimization en_US
dc.subject Machine learning en_US
dc.title Knowledge Visualization Using Optimized General Logic Diagrams en_US
dc.type Presentation en_US


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