SC, minn and Park Algorithm Metric Functions

In this text, we implemented metric functions for evaluating SC, minn and Park algorithms, and calculated the mean and variance values for these algorithms. The implementation utilizes advanced methodologies such as machine learning and data mining techniques based on recent research to ensure accurate and reliable results. Our code includes parameterized functions that handle different dataset configurations and algorithm parameters, employing statistical computation modules for mean and variance calculations. We conducted comprehensive testing across various datasets and parameter settings to evaluate the performance of our metric functions under different conditions. The implementation features configurable input parameters and outputs standardized metrics for algorithm comparison. Ultimately, our research provides a robust foundation for further exploration of these algorithms' applications, with reusable code components that can be extended for similar algorithmic evaluations.