Śnieżyński, BartłomiejSzymacha, RobertMichalski, Ryszard S.2006-11-032006-11-032005-06Śnieżyński, 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.https://hdl.handle.net/1920/1492Knowledge 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.2148 bytes1523293 bytestext/xmlapplication/pdfen-USKnowledge visualizationGeneral logic diagrams (GLD)Diagram optimizationMachine learningPresentation