Data mining method for English classroom teaching quality based on hierarchical clustering

English classroom teaching involves multiple types of data, and effectively collecting and organising these data is a challenging task. Therefore, a hierarchical clustering based data mining method for English classroom teaching quality is proposed. Firstly, use dynamic layered distributed data collection algorithms to collect data; Secondly, use a moving average filter to smooth the data, transform the data through Fourier transform, and calculate the threshold for outliers based on normal distribution to achieve data outlier handling. Then, the recursive feature elimination method is used to perform feature selection on the data, and linear discriminant analysis is used to perform feature dimensionality reduction. Finally, use hierarchical clustering algorithm for data mining. The experimental results show that the recall rate of this method is high, the mean square error is low, the data storage space occupied is low, indicating that this method can effectively improve the effectiveness of teaching mining.