Title: Parallel clustering method for complex attribute network big data based on transfer learning

Authors: Zhicheng Jia

Addresses: Gansu University of Political Science and Law, Lanzhou, 730070, China

Abstract: Complex attribute network data has high-dimensional features, with a large number of redundant or weakly correlated attributes, which increases computational complexity, increases storage requirements and computation time, and affects the efficiency and accuracy of clustering. Therefore, a parallel clustering method for complex attribute network big data based on transfer learning is proposed. In the data preprocessing stage, high-dimensional load attribute network data undergoes dimensionality reduction, denoising, and standardisation to reduce data complexity and improve data quality. We introduce deep learning technology and transfer learning theory to construct a parallel clustering analysis model, enabling rapid processing and analysis of large-scale data. The experimental results show that the clustering accuracy of the proposed method is higher than 0.94, and the NML value is higher than 0.95, effectively improving clustering performance, demonstrating greater application value, and providing a new approach for network big data clustering.

Keywords: transfer learning; complex attribute network; big data mining; parallel clustering.

DOI: 10.1504/IJBIDM.2025.149089

International Journal of Business Intelligence and Data Mining, 2025 Vol.27 No.2/3/4, pp.228 - 243

Received: 25 Nov 2024
Accepted: 18 Jun 2025
Published online: 13 Oct 2025 *

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