Research on Library Long-tail Server Based on K-means Algorithms

被引:1
|
作者
Jia, Yao [1 ]
Yao, Jiayi [1 ]
Li, Yajing [1 ]
机构
[1] Beijing Jiaotong Univ, Sch Econ & Management, Beijing 100044, Peoples R China
关键词
Data Mining; Long Tail Theory; Library Service;
D O I
10.1063/1.5090733
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The rapid development of Internet affects all walks of life in our society, And the library service industry is the first to be affected, Electronic information publishers, together with Internet content providers are all competing in taking over the book information market actively, the development of the situation the Library facing is very serious, Taking advantages of the combination of library's traditional superiority with advanced technology will be the key pose to mend matters, Needless to say that it is a very arduous task, This paper uses the method of data mining to explore how to apply the long tail theory to the library service field and put forward the concept of "library long tail service", On the basis of analyzing the applicability of the long tail theory in the field of library service, this paper puts forward the library long tail service data mining system based on the keyword text clustering, and according to the characteristics of the data generated by the library service, In the library long tail service research theory, methods and techniques, and carried out the algorithm improvement.
引用
收藏
页数:5
相关论文
共 50 条
  • [21] Research on Improved K-Means Algorithm Based on Hadoop
    Wei Xiaojing
    Li Yuanbo
    [J]. 2017 4TH INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND CONTROL ENGINEERING (ICISCE), 2017, : 593 - 598
  • [22] Quantum k-means algorithm based on trusted server in quantum cloud computing
    Changqing Gong
    Zhaoyang Dong
    Abdullah Gani
    Han Qi
    [J]. Quantum Information Processing, 2021, 20
  • [23] K-means Based Edge Server Deployment Algorithm for Edge Computing Environments
    Li, Bo
    Wang, Keyue
    Xue, Duan
    Pei, Yijian
    [J]. 2018 IEEE SMARTWORLD, UBIQUITOUS INTELLIGENCE & COMPUTING, ADVANCED & TRUSTED COMPUTING, SCALABLE COMPUTING & COMMUNICATIONS, CLOUD & BIG DATA COMPUTING, INTERNET OF PEOPLE AND SMART CITY INNOVATION (SMARTWORLD/SCALCOM/UIC/ATC/CBDCOM/IOP/SCI), 2018, : 1169 - 1174
  • [24] Quantum k-means algorithm based on trusted server in quantum cloud computing
    Gong, Changqing
    Dong, Zhaoyang
    Gani, Abdullah
    Qi, Han
    [J]. QUANTUM INFORMATION PROCESSING, 2021, 20 (04)
  • [25] Research on k-means Clustering Algorithm An Improved k-means Clustering Algorithm
    Shi Na
    Liu Xumin
    Guan Yong
    [J]. 2010 THIRD INTERNATIONAL SYMPOSIUM ON INTELLIGENT INFORMATION TECHNOLOGY AND SECURITY INFORMATICS (IITSI 2010), 2010, : 63 - 67
  • [26] Comparative Study of K-Means, Pam and Rough K-Means Algorithms Using Cancer Datasets
    Kumar, Parvesh
    Wasan, Krishan
    [J]. COMPUTING, COMMUNICATION, AND CONTROL, 2011, 1 : 136 - 140
  • [27] A Comparative Study of K-Means, K-Means plus plus and Fuzzy C-Means Clustering Algorithms
    Kapoor, Akanksha
    Singhal, Abhishek
    [J]. 2017 3RD IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE & COMMUNICATION TECHNOLOGY (CICT), 2017,
  • [28] Fast k-means algorithms with constant approximation
    Song, MJ
    Rajasekaran, S
    [J]. ALGORITHMS AND COMPUTATION, 2005, 3827 : 1029 - 1038
  • [29] A Survey on Various K-Means algorithms for Clustering
    Singh, Malwinder
    Bansal, Meenakshi
    [J]. INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY, 2015, 15 (06): : 60 - 65
  • [30] Faster algorithms for the constrained k-means problem
    Bhattacharya, Anup
    Jaiswal, Ragesh
    Kumar, Amit
    [J]. Leibniz International Proceedings in Informatics, LIPIcs, 2016, 47