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Nordiyya Ellwanger Hamerkaz
Ruppin Academic Center, Israel
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Transportation

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Hybrid Grid Partitioning and Rough Set Method for Fuzzy Rule Generation: Enhancing Dataset Clustering and Interpretability in Data Analysis Nordiyya Ellwanger Hamerkaz
International Journal of Enterprise Modelling Vol. 14 No. 2 (2020): May: 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.v14i2.31

Abstract

This research focuses on the development of a hybrid approach that combines grid partitioning, rough set theory, and fuzzy rule generation to enhance dataset clustering accuracy and improve the interpretability of generated rules in data analysis. The integration of grid partitioning techniques improves clustering accuracy by reducing the search space and efficiently identifying data patterns and relationships. Incorporating rough set theory facilitates attribute reduction, reducing the dimensionality of the dataset and enhancing interpretability. Fuzzy rule generation enables linguistic representation, allowing for human-understandable explanations. The proposed approach addresses the limitations of traditional methods, providing a comprehensive framework for accurate clustering and interpretable rule generation. The significance and potential benefits of the approach are discussed, along with its limitations and future directions. Numerical examples and findings demonstrate the effectiveness of the hybrid approach in enhancing dataset clustering accuracy and interpretability. The research contributes to advancing the field of data analysis by providing a comprehensive framework for accurate and interpretable analysis of complex datasets.
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