MMR: An algorithm for clustering categorical data using Rough Set Theory
Data & Knowledge Engineering
The normal parameter reduction of soft sets and its algorithm
Computers & Mathematics with Applications
Letter to the editor: Comment on "A fuzzy soft set theoretic approach to decision making problems"
Journal of Computational and Applied Mathematics
On some new operations in soft set theory
Computers & Mathematics with Applications
The parameterization reduction of soft sets and its applications
Computers & Mathematics with Applications
An adjustable approach to fuzzy soft set based decision making
Journal of Computational and Applied Mathematics
A rough set approach for selecting clustering attribute
Knowledge-Based Systems
A framework on rough set-based partitioning attribute selection
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
On multi-soft sets construction in information systems
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Matrices representation of multi soft-sets and its application
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part III
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Soft-set theory proposed by Molodstov is a general mathematic tool for dealing with uncertainty. Recently, several algorithms have been proposed for decision making using soft-set theory. However, these algorithms still concern on Boolean-valued information system. In this paper, Support Attribute Representative SAR, a soft-set based technique for decision making in categorical-valued information system is proposed. The proposed technique has been tested on three datasets to select the best partitioning attribute. Furthermore, two UCI benchmark datasets are used to elaborate the performance of the proposed technique in term of executing time. On these two datasets, it is shown that SAR outperforms three rough set-based techniques TR, MMR, and MDA up to 95% and 50%, respectively. The results of this research will provide useful information for decision makers to handle categorical datasets.