An empirical study of improved ant colony clustering algorithm in English composition review

Abstract

The scoring analysis method of English composition review lacks flexibility. To solve this problem, this paper proposes an analysis method based on the improved ant colony clustering algorithm, where cosine distance and Euclidean distance were combined to determine the conversion function. The empirical results show that compared with the previous standard ant colony clustering algorithm, the traditional k-means algorithm and IGKA algorithm, the improved ant colony clustering algorithm can realise the comprehensive evaluation of English composition review. It can be seen that the proposed method is reasonable and feasible, which can effectively conduct cluster analysis on English composition review, and has a higher accuracy rate of 89.33%. Therefore, in order to achieve the clustering analysis of English composition rating more precisely, the next step is to improve the ant colony clustering algorithm by repeated experiments on experimental data.