| Issue |
MATEC Web Conf.
Volume 252, 2019
III International Conference of Computational Methods in Engineering Science (CMES’18)
|
|
|---|---|---|
| Article Number | 03019 | |
| Number of page(s) | 7 | |
| Section | Computational Artificial Intelligence | |
| DOI | https://doi.org/10.1051/matecconf/201925203019 | |
| Published online | 14 January 2019 | |
Comparative Overview of Rough Set Toolkit Systems for Data Analysis
AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Krakow, Poland
* Corresponding author: pipieta@agh.edu.pl
Inconsistency, lacking values of attributes or parameters, as well as discrepancies between records caused by insufficient precision cannot always be managed in the initial phases of knowledge discovery, i.e., data preparation and refinement. The theory of rough sets aims to overcome problems that are caused by uncertainty and lack of precision within the gathered data sets. This approach is a useful tool that operates on a formal model using relational algebra, elementary operations on finite sets and first-order logic. In this paper, we present an analysis of existing rough set tools, namely: Rough Set Exploration System, Rough Sets Data Explorer, Rough Set Data Analysis Framework, Waikato Environment for Knowledge Analysis and Rough Set Toolkit for Analysis of Data. Our comparison is performed only theoretically and covers the available algorithms, preparation of input data, licensing, as well as installation requirements.
© The Authors, published by EDP Sciences, 2019
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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