Application of FCM Clustering Algorithm in Digital Library Management System

Traditional library systems are gradually being replaced by digital libraries. Digital libraries are developing from simple database-based storage and retrieval to knowledge-based implementation. The fuzzy C-means (FCM) clustering algorithm is an example of data collection and data processing techno...

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Vydané v:Electronics (Basel) Ročník 11; číslo 23; s. 3916
Hlavný autor: Shi, Yuqing
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.12.2022
Predmet:
Algorithms
Archives & records
Borrowing
Clustering
Data base management systems
Data collection
Data mining
Data processing
Digital libraries
Digital systems
Efficiency
Information management
Libraries
Library administration
Library collections
Library management
Library resources
Management
Readers
Reading
Technical services
Technology assessment
User interface
Validity
China
Jakarta Indonesia
Indonesia
ISSN:2079-9292, 2079-9292
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Shrnutí:Traditional library systems are gradually being replaced by digital libraries. Digital libraries are developing from simple database-based storage and retrieval to knowledge-based implementation. The fuzzy C-means (FCM) clustering algorithm is an example of data collection and data processing technology. It evaluates and draws conclusions based on mathematics, large data, and other technologies. In order to better improve the digital library management system, this paper applied FCM clustering algorithm to the digital library management system. Based on the in-depth study of the FCM clustering algorithm, this paper built a digital library management system. The clustering algorithm was used to cluster library borrowing records and reader information. It provided technical support and suggestions on library collection construction and book purchase and promoted book management to form a good spitting cycle. The experimental results extracted during the evaluation phase demonstrated that the overall error rate of the suggested FCM clustering algorithm for information clustering is 3.66%, which is better than the existing comparative models. This shows that applying the FCM clustering algorithm to a digital library management system has some practical significance.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11233916