Attribute Reduction Program Based on Rough Set Theory

This is a MATLAB-implemented program designed for attribute reduction based on rough set theory. The core functionality of this program processes given datasets by reducing attributes while preserving the essential characteristics of the original data. Built upon rough set theory—a mathematical framework for handling uncertain and incomplete data—the program employs key algorithms such as dependency degree calculation, discernibility matrices, and attribute significance evaluation to identify minimal attribute subsets. Through attribute reduction, users can gain deeper insights into dataset patterns, discover underlying trends and regularities, and obtain support for data analysis and decision-making processes. The implementation includes core functions like computeDependency() for measuring attribute importance, buildDiscernibilityMatrix() for identifying distinguishable object pairs, and reduceAttributes() for executing the reduction algorithm. Additionally, the program incorporates essential data preprocessing and analysis capabilities, including data cleaning functions to handle missing values and outliers, and visualization modules for displaying attribute dependencies and reduction results. These auxiliary features help users better understand and analyze data structures throughout the reduction process.