• Arunava Bhattacharjee
• Sharmistha Bhattacharya Halder
• Tandra Sarkar

Rough Set Theory (RST) has emerged as a powerful mathematical tool for dealing with vagueness and uncertainty in data analysis. Since its inception, RST has evolved through numerous extensions and hybrid models that aim to enhance its capacity in complex decision-making environments. This review presents a comprehensive overview of the evolution of RST—from its classical foundations to modern developments such as fuzzy rough sets, probabilistic rough sets, covering-based models, and information-theoretic variants. The study critically examines the inherent limitations of traditional RST, explores its integration with contemporary computational frameworks, and identifies open challenges and promising research directions for future advancement.

Rough Set Theory; Attribute Reduction; Uncertainty Modeling; Hybrid Rough Models; Extensions of RST; Knowledge Discovery

"Rough Set Theory: Past, Present, and Future - A Comprehensive Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 10, page no.d268-d281, October-2025, Available :http://www.jetir.org/papers/JETIR2510337.pdf

"Rough Set Theory: Past, Present, and Future - A Comprehensive Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 10, page no. ppd268-d281, October-2025, Available at : http://www.jetir.org/papers/JETIR2510337.pdf