The integration algorithm of digital resources in business administration based on cluster analysis.

The article focuses on a clustering analysis-based digital resource integration algorithm designed to improve the management of business administration data by unifying scattered digital resources from various sources. It proposes a three-layer framework—data collection, integration via preprocessing and feature extraction using a multi-label dimensionality reduction method (MDDMp), and secure storage employing the Security Information Diffusion Algorithm (IDA)—to enhance data quality, classification, and security. Experimental results using real industrial and commercial administration data demonstrate that the algorithm achieves low data redundancy (under 8%), minimal integration difference (below 11%), and efficient processing time (less than 2.11 minutes), outperforming several existing methods in accuracy and speed. The study highlights the algorithm's potential for effective digital resource integration in business management while noting the need for further research on multi-source data heterogeneity and privacy protection.