Rough Set Conditional Attribute Dependency Degree Calculation Function for Decision Attributes

This dependency degree calculation function for conditional attributes on decision attributes in rough set theory is designed for simplicity and practical usability, enabling efficient data analysis. The implementation typically involves calculating the positive region of decision classes with respect to conditional attributes, where dependency degree γ = |POS_C(D)| / |U| (with POS representing positive region, C for conditional attributes, D for decision attributes, and U for universe). The method efficiently handles both small-scale and large-scale datasets through optimized set operations and approximation algorithms. Users can rapidly identify key attributes using this function's core methods like attribute reduct identification and significance calculation. The function's extensible architecture supports integration with various domains including business intelligence (for customer segmentation), financial risk assessment, and medical diagnosis systems through modular dependency calculation modules and custom attribute weighting mechanisms.