RuleVisualization is a decision support tool for visualization and exploration of monotonic decision rules induced in multi-criteria ordinal classification problems (also known as sorting). Is is based on the ruleLearn library, which contains up-to-date implementations of methods of data analysis provided by the Dominance-based Rough Set Approach and Variable Consistency Dominance-based Rough Set Approaches.
RuleVisualization is a client-server application. Server is written in Java and incorporates ruleLearn library. Client is a single page JavaScript application, written using Vue.js framework, that can be run in a HMTL5 compliant web browser.
This application has been implemented by master student Mateusz Lewandowski, as part of his master thesis defended in October 2019 (under the supervision of Ph.D. Marcin Szeląg).
RuleVisualization accepts three input files:
JSON file with metadata (attributes),
XML file with decision rules in RuleML format (with or without characteristics),
JSON file with test set objects (optional) against which rules can be matched.
For further information please refer to the user documentation linked below.
Here is the list of currently available downloads for RuleVisualization. By downloading the software you accept this license agreement.
RuleVisualization-release (GitHub) - released versions of RuleVisualization application (compiled from server and client source code), ready to download and use (containing exemplary data sets and rules to visualize)
RuleVisualization-server (GitHub) - source code of server application (backend)
RuleVisualization-client (GitHub) - source code of client application (frontend)
RuleVisualization-documentation (GitHub) - user documentation (Latex sources and pdf releases)
In case you need further assistance, you can contact me by e-mail.
Below is the list of publications concerning the methodology used in RuleVisualization.
M. Szeląg, J. Błaszczyński, R. Słowiński, Rough Set Analysis of Classification Data with Missing Values. [In]: L. Polkowski et al. (Eds.): Rough Sets, International Joint Conference, IJCRS 2017, Olsztyn, Poland, July 3–7, 2017, Proceedings, Part I. Lecture Notes in Artificial Intelligence, vol. 10313, Springer, 2017, pp. 552–565. (manuscript)
J. Błaszczyński, R. Słowiński, M. Szeląg, Induction of Ordinal Classification Rules from Incomplete Data. [In]: J.T. Yao et al. (Eds.): Rough Sets and Current Trends in Computing 2012. Lecture Notes in Artificial Intelligence, vol. 7413, Springer, Berlin Heidelberg, 2012, pp. 56–65.
J. Błaszczyński, R. Słowiński, M. Szeląg, Sequential Covering Rule Induction Algorithm for Variable Consistency Rough Set Approaches. Information Sciences, 181, 2011, pp. 987-1002, doi:10.1016/j.ins.2010.10.030. (manuscript)
J. Błaszczyński, R. Słowiński, M. Szeląg, VC-DomLEM: Rule induction algorithm for variable consistency rough set approaches. Research Report RA-07/09, Poznań University of Technology, 2009. (full text)
J. Błaszczyński, S. Greco, R. Słowiński, M. Szeląg, Monotonic Variable Consistency Rough Set Approaches. International Journal of Approximate Reasoning, 50(7), 2009, pp. 979-999.
J. Błaszczyński, S. Greco, R. Słowiński, M. Szeląg, Monotonic Variable Consistency Rough Set Approaches. [In]: J. Yao, P. Lingras , W. Wu, M. Szczuka, N. J. Cercone, D. Ślęzak (eds.), Rough Sets and Knowledge Technology 2007. Lecture Notes in Artificial Intelligence, vol. 4481, Springer, Berlin Heidelberg, 2007, pp. 126-133.
J. Błaszczyński, S. Greco, R. Słowiński, M. Szeląg, Monotonic Variable Consistency Rough Set Approaches. Research Report RA-010/07, Poznań University of Technology, 2007.
J. Błaszczyński, S. Greco, R. Słowiński, M. Szeląg, On Variable Consistency Dominance-based Rough Set Approaches. [In]: S. Greco, Y. Hata, S. Hirano, M. Inuiguchi, S. Miyamoto, H. S. Nguyen, R. Słowiński (eds.), Rough Sets and Current Trends in Computing 2006. Lecture Notes in Artificial Intelligence, vol. 4259, Springer, Berlin 2006, pp. 191-202.
R. Słowiński, S. Greco, B. Matarazzo, Rough Set Based Decision Support. Chapter 16 [in]: E. K. Burke, G. Kendall (eds.), Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques. Springer, New York, 2005, pp. 475-527.
S. Greco, B. Matarazzo, R. Słowiński, Rough Sets Theory for Multicriteria Decision Analysis. European Journal of Operational Research, 129(1), 2001, pp. 1-47.