Intelligent Database Feature Mining Method Based on Genetic Clustering Algorithm

Abstract

The intelligent database feature mining method relies on preset thresholds, resulting in the omission of key features and affecting classification performance. Therefore, research on intelligent database feature mining methods based on genetic clustering algorithm. Firstly, a balanced processing method is adopted for the data in the database to preserve key information. Then, set the autocorrelation function and combine it with the data hierarchy and attribute integration relationship to accurately extract useful features from the database. Finally, the genetic clustering algorithm is applied to iteratively classify database features, and the clustering results are continuously optimized by combining genetic algorithm and K-means clustering algorithm to obtain the optimal or approximately optimal clustering scheme. The experimental results show that our method outperforms the three compared methods in terms of classification accuracy and execution efficiency, providing an efficient and reliable solution for feature mining of large-scale data.