Improved k-means-based FAKM clustering method for scientific and technical literature

Research on rapid clustering technology based on bibliographic information of scientific and technical literature aims to efficiently realize the correlation analysis of scientific and technical literature, laying the foundation for discovering hot spots and trends in the research field, conducting interdisciplinary and cross-border research, and accurately recommending scientific and technical literature. Focusing on the analysis of clustering algorithms, we proposed an improved k-meansbased Firefly Algorithm k-means (FAKM) clustering method, which effectively solved the problem of randomly selecting the initial center points of class cluster when using k-means algorithm for clustering in the clustering stage, which leads to local optimum, low accuracy and large gap between the division of class clusters and the real situation of clustering results. The use of FAKM clustering algorithm resulted in better clustering performance, high accuracy, and fewer iterations. The experimental results showed that the method achieved a silhouette coefficient of 0.54 and adjust mutual information of 0.69 on the same scientific and technical literature data set, which proved the good performance of the method.