关键词: 特征选择, 蜣螂优化算法, 二进制麻雀搜索算法, ｋｍｅａｎｓ＋＋, 文本聚类, 特征词权重

Abstract: This paper proposes a feature selection and text clustering algorithm that improves the binary sparrow search algorithm by combining the dung beetle optimization algorithm to address the issue of redundant features affecting clustering accuracy in text. The algorithm first uses the fitness function based on the weight of feature words to complete the text feature evaluation and build a vector space model; Then, the circular direction search mechanism in the dung beetle optimization algorithm is introduced to improve the position update strategy of the sparrow finder in the traditional sparrow search algorithm, and the random walk strategy with the rolling direction mechanism is integrated to improve the global search ability. Combined with the transfer function, the continuous final sparrow position is updated to obtain the optimized binary sparrow search algorithm, and high-quality feature subsets are screened out; Finally, the k-means++algorithm was selected to complete text clustering. Validated through multiple benchmark functions and public dataset experiments. The results indicate that the method proposed in this article can effectively reduce the dimension of text features and improve clustering performance.