Title: A data mining method for biomedical literature based on association rules algorithm

Authors: Xiaofeng Shi; Yaohong Zhao; Haijuan Du

Addresses: Centre of Modern Education Technology, Changchun Institute of Technology, Changchun, 130012, China ' Faculty of Computer Science and Technology, Changchun University, Changchun, 130022, China ' School of Information Science and Engineering, Jiaxing University, Jia'xing, 314001, China

Abstract: There are problems in the process of biomedical literature data mining, such as high data noise, low mining accuracy, and long-time consumption. Therefore, a biomedical literature data mining method based on association rule algorithm was designed. First, set up the extraction process of biomedical literature data, introduce the factor graph decomposition global extraction function, and establish a probabilistic database to speed up the extraction. Secondly, wavelet transform is used to denoise the data, improve the effectiveness of the extracted data, and classify it based on its importance. Finally, by setting association rules for biomedical literature data mining and introducing pre pruning methods on this basis, the time cost of calculating support is reduced, mining efficiency is improved, and combining confidence and dependency, a biomedical literature data mining model based on association rules is constructed to achieve the final mining. The results show that this method improves the accuracy of literature mining, reaching 99%, and effectively reduces the mining time, with a maximum time consumption of 1.7 seconds. It has strong application performance.

Keywords: association rules; biomedical literature; data mining; wavelet transform; vector space; classification basis.

DOI: 10.1504/IJDMB.2024.136220

International Journal of Data Mining and Bioinformatics, 2024 Vol.28 No.1, pp.1 - 17

Received: 30 Dec 2022
Accepted: 04 May 2023
Published online: 22 Jan 2024 *