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

Baosheng Yin; Meishu Zhao

doi:10.1117/12.2692027

21 August 2023 Improved k-means-based FAKM clustering method for scientific and technical literature

Baosheng Yin, Meishu Zhao

Proceedings Volume 12783, International Conference on Images, Signals, and Computing (ICISC 2023); 127830L (2023) https://doi.org/10.1117/12.2692027
Event: International Conference on Images, Signals, and Computing (ICISC2023), 2023, Chengdu, China

Abstract

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.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Baosheng Yin and Meishu Zhao "Improved k-means-based FAKM clustering method for scientific and technical literature", Proc. SPIE 12783, International Conference on Images, Signals, and Computing (ICISC 2023), 127830L (21 August 2023); https://doi.org/10.1117/12.2692027

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