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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
C.L. Blake and C.J. Merz. UCI repository of machine learning databases, 1998.
R. Davidson and D. Harel. Drawing graphs nicely using simulated annealing. ACM Transactions on Graphics, 15(4):301–331, 1996.
K. A. Kaufman and R. S. Michalski. The development of the inductive database system VINLEN: A review of current research. In M. Kłopotek et al., editor, Intelligent Information Processing and Web Mining, Advances in Soft Computing, pages 393–398. Springer, 2003.
S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi. Optimization by genetic annealing. Science, (220):671–680, 1983.
A. Marquand. On logical diagrams for n terms. The London, Edinburgh and Dublin Philosophical Magazine and Journal of Science, XII(75):266–270, 1881.
R. S. Michalski. Graphical minimization of normal expressions of logic functions using tables of the veitch-karnaugh type. Journal of the Institute of Automatic Control, (52), 1978.
R. S. Michalski. A planar geometrical model for representing multi-dimensional discrete spaces and multiple-valued logic functions. Technical Report 897, Department of Computer Science, University of Illinois, Urbana, 1978.
R. S. Michalski. Attributional Calculus: A Logic and Representation Language for Natural Induction. Reports of the Machine Learning and Inference Laboratory, MLI 04-2. George Mason University, 2004.
R. S. Michalski, K. A. Kaufman, and G. Cervone. Speeding up evolution through learning: LEM. In M. Kłopotek et al., editor, Proceedings of the Ninth International Symposium on Intelligent Information Systems, 2000.
I. Mierswa, R. Klinkberg, S. Fischer, and O. Ritthoff. A Flexible Platform for Knowledge Discovery Experiments: YALE — Yet Another Learning Environment. In LLWA 03-Tagungsband der GI-Workshop-Woche Lernen-Lehren-Wissen-Adaptivitt, 2003.
I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, 1999.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Śnieżyński, B., Szymacha, R., Michalski, R.S. (2005). Knowledge Visualization Using Optimized General Logic Diagrams. In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds) Intelligent Information Processing and Web Mining. Advances in Soft Computing, vol 31. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32392-9_15
Download citation
DOI: https://doi.org/10.1007/3-540-32392-9_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25056-2
Online ISBN: 978-3-540-32392-1
eBook Packages: EngineeringEngineering (R0)