Grey Wolf Optimization for Data Mining

Abstract

In the last several decades, the development of innovative optimization techniques that were successfully utilized to handle such stochastic mining challenges sparked a lot of interest in data mining optimization. Data mining is the method of identifying data trends with the help of specialized systems. Many research has already demonstrated a range of data mining techniques, but none has indicated which optimization strategy delivers superior data mining results. Researchers offer the grey wolf optimization approach for data mining in this study. The flowchart and mathematical equation for the Grey Wolf Optimizer (GWO) algorithm with training Multi-layer Perceptron (MLP) neural network are presented in this study. This study also explains why GWO is the best optimization approach for data mining jobs, using factors like as run time, Mean Square Error (MSE), and classification rate to support its claim. Hybrid algorithms, which integrate two distinct optimization techniques, can help Data mining jobs in the future.