Please wait a minute...
New Technology of Library and Information Service  2010, Vol. 26 Issue (2): 7-11    DOI: 10.11925/infotech.1003-3513.2010.02.02
article Current Issue | Archive | Adv Search |
Market Segmentation of Digital Library Users Based on Concept Lattice ——Conceptual Clustering Analysis of Digital Library Users
Teng Guangqing   Bi Qiang
(School of Management，Jilin University, Changchun 130022，China)
Download:
Export: BibTeX | EndNote (RIS)      
Abstract  

Based on concept lattice theory and drawing supports from market segmentation variables of marketing, this article develops market segment of digital library users by means of conceptual clustering of formal concept analysis. The authors also investigate the construction of elastic segmentation mechanism by breakthrough of traditional statistics in digital library users.
Key words： Concept lattice      Digital library      Users      Market segmentation      Clustering analysis     
Received: 23 December 2009      Published: 25 February 2010
: 

G250.76

 		 
Corresponding Authors: Bi Qiang     E-mail: biqiang12345@163.com
About author:: Teng Guangqing,Bi Qiang
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
BI Jiang

Cite this article:

Teng Guangqing,Bi Qiang. Market Segmentation of Digital Library Users Based on Concept Lattice ——Conceptual Clustering Analysis of Digital Library Users. New Technology of Library and Information Service, 2010, 26(2): 7-11.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2010.02.02     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2010/V26/I2/7

［1］ Borgman C L. What are Digital Libraries? Competing Visions［J］. Information Processing and Management，1999，35 (3):227-243.
［2］张晓林. 数字图书馆机制的范式演变及其挑战［J］. 中国图书馆学报, 2001，27(6):3-5.
［3］ Smith W R. Product Differentiation and Market Segmentation as Alternative Marketing Strategies［J］. The Journal of Marketing，1956，21(1):3-8.
［4］Stumme G. Formal Concept Analysis on Its Way from Mathematics to Computer Science［C］. In：Proceedings of the 10th International Conference on Conceptual Structures: Integration and Interfaces. London, UK：Springer-Verlag，2002:2-19.
［5］ Wille R. Restructuring Lattice Theory: An Approach Based on Hierarchies of Concepts［C］. In: Proceedings of the 7th International Conference on Formal Concept Analysis. Berlin: Springer-Verlag, 2009: 314-339.
［6］ Strahringer S, Wille R. Conceptual Clustering via Convex-ordinal Structures［C］. In: Proceedings of FGML Workshop. Berlin-Heidelberg：Springer, 1993:85-98.
［7］ Mineau G, Godin R. Automatic Structuring of Knowledge Bases by Conceptual Clustering［J］. IEEE Transactions on Knowledge and Data Engineering, 1995, 7(5):824-829.
［8］ Stumme G, Taouil R, Bastide Y, et al. Computing Iceberg Concept Lattices with Titanic［J］. Data and Knowledge Engineering, 2002,42(2):189-222.
［9］ Ganter B, Wille R. Formal Concept Analysis［M］. Berlin-Heidelberg：Springer-Verlag, 1999.
［10］张正. 数字图书馆用户研究［J］. 图书馆,2004(1):46-48.
[1] Shan Xiaohong,Wang Chunwen,Liu Xiaoyan,Han Shengxi,Yang Juan. Identifying Lead Users in Open Innovation Community from Knowledge-based Perspectives[J]. 数据分析与知识发现, 2021, 5(9): 85-96.
[2] Wu Jinming,Hou Yuefang,Cui Lei. Automatic Expression of Co-occurrence Clustering Based on Indexing Rules of Medical Subject Headings[J]. 数据分析与知识发现, 2020, 4(9): 133-144.
[3] Cai Yongming,Liu Lu,Wang Kewei. Identifying Key Users and Topics from Online Learning Community[J]. 数据分析与知识发现, 2020, 4(6): 69-79.
[4] Liu Ping,Peng Xiaofang. Calculating Word Similarities Based on Formal Concept Analysis[J]. 数据分析与知识发现, 2020, 4(5): 66-74.
[5] Weicong Lu,Jian Xu. Sentiment Analysis for Online User Reviews Based on Tripartite Network[J]. 数据分析与知识发现, 2019, 3(8): 10-20.
[6] Jiang Wu,Yinghui Zhao,Jiahui Gao. Research on Weibo Opinion Leaders Identification and Analysis in Medical Public Opinion Incidents[J]. 数据分析与知识发现, 2019, 3(4): 53-62.
[7] Xinrui Wang,Yue He. Predicting Stock Market Fluctuations with Social Media Behaviors: Case Study of Sina Finance Blog[J]. 数据分析与知识发现, 2019, 3(11): 108-119.
[8] Wang Feifei,Zhang Shengtai. Analyzing Information Behaviors of Mobile Social Network Users[J]. 数据分析与知识发现, 2018, 2(4): 99-109.
[9] Qi Yunfei,Zhao Yuxiang,Zhu Qinghua. Linked Data for Mobile Visual Search System of Digital Library[J]. 数据分析与知识发现, 2017, 1(1): 81-90.
[10] Hong Liang,Qian Chen,Fan Xing. Context-aware Recommendation System for Mobile Digital Libraries[J]. 现代图书情报技术, 2016, 32(7-8): 110-119.
[11] Wang Yuefen,Jia Xinlu,Fu Zhu. Content Using Behavior of Academic Social Network System: Case Study of Popular Blogs from Sciencenet.cn[J]. 现代图书情报技术, 2016, 32(6): 63-72.
[12] Liu Jian,Bi Qiang,Ma Zhuo. Assessment of Digital Library’s Micro-services: An Empirical Study[J]. 现代图书情报技术, 2016, 32(5): 22-29.
[13] Lu Jiaying,Yuan Qinjian,Huang Qi,Qian Yunjie. Building Product Domain Ontology with Concept Lattice Theory[J]. 现代图书情报技术, 2016, 32(5): 38-46.
[14] He Jianmin,Yin Shu. Identifying Influential Users in Social Networks[J]. 现代图书情报技术, 2016, 32(4): 20-30.
[15] Liu Pingfeng,Wang Bei,Lei Jie. Using Latent Class Analysis to Classify O2O Service Users[J]. 现代图书情报技术, 2016, 32(2): 52-58.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626，82624938   E-mail:jishu@mail.las.ac.cn