Cloud Data Resources and Library Subject Information Services

• Published in: Applied mathematics and nonlinear sciences Vol. 9; no. 1
• Main Author: Zhang, Chen
• Format: Journal Article
• Language: English
• Published: Beirut Sciendo 01.01.2024; De Gruyter Poland
• Subjects: 62-07; Apriori algorithm; Association rules; k-means algorithm; Library information service; User portrait
• ISSN: 2444-8656; 2444-8656
• DOI: 10.2478/amns-2024-1007

In the evolving landscape of library services, propelled by advancements in Internet technology and service paradigms, this study utilizes cloud-based lending data from college libraries to improve user profiling and subject-specific lending. Integrating the K-means algorithm with a Boolean matrix-enhanced Apriori algorithm, we devise a data mining model that fine-tunes detecting patterns in user borrowing behaviors. This approach distinguishes five distinct subject areas: energy, computing, electronic communication, machinery, and environmental chemistry. The outcome reveals a bibliographic association rule mining confidence of up to 79.38%, a 30% increase over conventional methods. Moreover, it generates three notable 2-item sets. Our model introduces a groundbreaking way to offer personalized library services, significantly enriching the user experience with tailored subject information.