An improved algorithm for mining media content application patterns based on QPop increasing disk time domain segmentation and upgrading¹

Issue title: Artificial Intelligence as a maturing and growing technology: An urgent need for intelligent systems

Guest editors: X. Yuan and M. Elhoseny

Article type: Research Article

Authors: Xindi, Yang^a | Huanran, Du^{b; *}

Affiliations: [a] Macau University of Science and Technology, School of Humanities and Arts, Macau SAR, Macao | [b] Guangzhou Sport University, School of Sports Media, China

Correspondence: [*] Corresponding author. Du Huanran, Guangzhou Sport University, School of sports media, China. E-mail: [email protected].

Note: [1] Macau University of Science and Technology, School of Humanities and Arts.

Abstract: The intelligent scheduling algorithm for hierarchical data migration is a key issue in data management. Mass media content platforms and the discovery of content object usage patterns is the basic schedule of data migration. We add QPop, the dimensionality reduction result of media content usage logs, as content objects for discovering usage patterns. On this basis, a clustering algorithm QPop is proposed to increase the time segmentation, thereby improving the mining performance. We hired the standard C-means algorithm as the clustering core and used segmentation to conduct an experimental mining process to collect the ted QPop increments in practical applications. The results show that the improved algorithm has good robustness in cluster cohesion and other indicators, slightly better than the basic model.

Keywords: Data migration, media content, QPop, log mining

DOI: 10.3233/JIFS-189356

Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 3177-3184, 2021