International Journal of Man-Machine Studies
Data mining: concepts and techniques
Data mining: concepts and techniques
Machine Learning
Finding fuzzy classification rules using data mining techniques
Pattern Recognition Letters
ROSE - Software Implementation of the Rough Set Theory
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Spatial data methods and vague regions: A rough set approach
Applied Soft Computing
Induction of multiple criteria optimal classification rules for biological and medical data
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting
Information Sciences: an International Journal
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
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This study proposes a selection index technique, namely a compactness rate based on Rough Set Theory (RST), for improving data analysis, eliminating data amount and reducing the number of decision rule. This study uses an empirical real-case involving a personal investment portfolio to demonstrate the proposed method. The presented case includes 75 rules generated by the RST. The rules are vague and fragmentary, making it very difficult to interpret the information. Many rules have the same strength and number of support objects and condition parts. These are creating a critical problem for decision making. The new method proposed in this study not only enables the selection of interesting rules, but it also reduces the data amount, and offers alternative strategies that can help decision-makers analyze data.