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Szolga Mihaela Munteanu
Technical University of Cluj-Napoca, Rumania
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Transportation

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Journal : International Journal of Enterprise Modelling

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An Enhanced Hybrid Grid Partitioning and Rough Set Approach for Fuzzy Rule Generation in Complex Dataset Problem Szolga Mihaela Munteanu; Makeithappen Daniela Ioan
International Journal of Enterprise Modelling Vol. 14 No. 3 (2020): Sep: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (0.474 KB) | DOI: 10.35335/emod.v14i3.35

Abstract

This research presents an enhanced hybrid grid partitioning and rough set approach for fuzzy rule generation in complex datasets. Complex datasets pose challenges due to their high dimensionality, nonlinearity, and uncertainty, which traditional analysis techniques struggle to address. Fuzzy rule-based systems offer a promising solution, but generating effective fuzzy rules becomes increasingly difficult as the dataset complexity increases. To overcome these challenges, we propose a methodology that integrates hybrid grid partitioning and rough set theory. The hybrid partitioning technique captures global and local patterns, while rough set theory handles uncertainty and incomplete information. The approach generates accurate and interpretable fuzzy rules by optimizing the rule set's size and balancing accuracy and interpretability. Experimental evaluations demonstrate the approach's effectiveness in terms of rule accuracy, interpretability, and computational efficiency. The generated fuzzy rules provide valuable insights into complex relationships, aiding decision-making processes in various domains. The research contributes to the advancement of fuzzy rule generation techniques for complex datasets and offers a practical solution for knowledge extraction in complex systems.
Co-Authors Makeithappen Daniela Ioan