Effectiveness of the Implementations of a Data Mining Algorithm Based Peak Restriction Method

The rapid growth of data in various domains has necessitated the development of advanced algorithms to efficiently analyze and manage large datasets. One of the key challenges in data mining is dealing with peak values or outliers, which can distort the analysis and lead to inaccurate predictions or decisions. This paper investigates the effectiveness of a data mining-based peak restriction method designed to minimize the influence of extreme values in large datasets. The proposed method uses a combination of data preprocessing techniques and machine learning algorithms to identify and limit peak values, enhancing the robustness and accuracy of predictive models. We evaluate the method across different datasets, including time-series, transactional, and sensor data, to assess its generalizability and performance. The results demonstrate significant improvements in model accuracy and stability, especially in applications sensitive to outliers, such as anomaly detection and predictive maintenance. Our findings suggest that the peak restriction method is a promising tool for improving the quality of data-driven models, with potential applications in various industries including finance, healthcare, and IoT. Furthermore, we discuss the limitations of the approach and propose future directions for further optimization.