Title: Multi-link similar data mining and cleaning method based on Bayesian algorithm

Authors: Kaiku Wang; Congkuan Huang; Yang Yang

Addresses: Department of Computer Science and Technology, Beijing University of Aeronautics and Astronautics, Beijing 100191, China ' Department of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China ' Department of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China

Abstract: Aiming at the problem of low precision and large cleaning error in multi-link similar data mining, a multi-link similar data mining cleaning method based on Bayesian algorithm is proposed in this paper. On the basis of extracting multi-link data, the data is preprocessed and the similarity between the data is calculated. The data with high similarity is input into the Bayesian network, and the data cleaning process is completed according to the maximum likelihood value of the data. Experimental results show that the mining accuracy of the proposed method for similar data can reach 98.51%, and the cleaning error is about 1.22%, indicating that the proposed method can more effectively complete the mining and cleaning of similar data in multi-links.

Keywords: Bayesian algorithm; multi-link similar data; data mining; directed graph; maximum likelihood.

DOI: 10.1504/IJICT.2024.137206

International Journal of Information and Communication Technology, 2024 Vol.24 No.2, pp.145 - 155

Received: 21 Oct 2021
Accepted: 20 Dec 2021
Published online: 05 Mar 2024 *

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