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Data Mining Technology in Book Copyright Information Management Decision System.
- Source :
- Mobile Information Systems; 9/30/2021, p1-10, 10p
- Publication Year :
- 2021
-
Abstract
- Today is an era of data "big bang"; Internet information technology is widely used in various fields of society. As an indispensable spiritual food in people's daily life, books are increasing in number and scale. In order to better manage book information, people have introduced data mining technology. Based on this, this article takes the research and application of data mining technology in book copyright information management decision-making system as the theme, explores the role of data mining technology in book copyright information management, and aims to provide reference for our country's book copyright information management and decision-making. This article first introduces the common algorithms of data mining technology and then elaborates on the advantages and effectiveness of the association rule method in data mining. Aiming at some defects of the original Apriori algorithm of the association rule method, an improved Apriori algorithm is proposed. After taking the library book information management system and database of a university in our province as the experimental research object, the performance gap between the two algorithms is compared through experiments, and it is concluded that when the number of transaction set item records is less than 1400, the Apriori algorithm performs better, and when the number of records in the transaction set is greater than 1400, the improved Apriori algorithm is obviously more advantageous. The research results show that the introduction and application of data mining technology make the information management of books more efficient and convenient, and it is more convenient for the management and decision-making of book copyright information. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1574017X
- Database :
- Complementary Index
- Journal :
- Mobile Information Systems
- Publication Type :
- Academic Journal
- Accession number :
- 152743005
- Full Text :
- https://doi.org/10.1155/2021/4388235