期刊文献+

文献资源招标采购标段划分模型研究及实证 被引量:3

Bid-sections partition model for literature resources procurement and its empirical test
下载PDF
导出
摘要 文献资源招标采购标段划分对提高文献资源招标采购绩效,促进高效利用有限资金投入具有重要的意义.本文针对文献资源标段划分影响因素展开分析,根据文献资源标段划分的特点,引入了基于偏好思想的分段测度方法,构建了文献资源招标采购标段划分方法模型,并以某大学外文原版期刊采购为例,进行了实证分析,结果表明所建模型具有可操作性和实用性. Bid-sections partition of literature resources has a great impact on improving the bid-procurement performance of literature resources and promoting the efficient use of the limited funding. In this paper, factors affecting of bid-sections partition of literature resources are first analyzed, then, according to the analysis and the characteristics of bid-sections partition, a K-prototypes clustering algorithm is brought into this issue, and thirdly the bid-sections partition model for literature resource procurements is established. The empirical analysis is made through the procurement data of foreign journals of some university and the results show that the proposed model is feasible and practical.
出处 《系统工程学报》 CSCD 北大核心 2012年第4期543-551,共9页 Journal of Systems Engineering
基金 黑龙江省自然科学基金重点资助项目(ZD200803-01) 黑龙江省高等教育学会"十一五"教育科学研究规划重点资助项目(115E-048) 黑龙江省高校图工委重点资助项目(2009B07)
关键词 文献资源 招标采购 标段划分 聚类分析 literature resources bid procurement bid-sections partition cluster analysis
  • 相关文献

参考文献21

  • 1胡永强,张洪钢.高校图书馆图书采购招标工作研究[J].图书馆学刊,2008,30(6):121-122. 被引量:12
  • 2叶莉.高校图书馆文献资源采购招标的对策研究[J].图书馆论坛,2006,26(1):114-115. 被引量:34
  • 3顾健.高校图书馆中文图书采购招标中的几个问题[J].大学图书馆学报,2007,25(3):15-20. 被引量:34
  • 4Budayan C, Dikmena I, Birgonula M T. Comparing the performance of traditional cluster analysis, self-organizing maps and fuzzy C-means method for strategic grouping[J]. Expert Systems with Applications, 2009, 36(9): 11772-11781.
  • 5Bittmann R M. Decision-making method using a visual approach, for cluster analysis problem: Indicative classification algorithms and grouping scope[J]. Expert Systems, 2007, 24(3): 172-188.
  • 6Girish P, David W S. Cluster analysis in marketing research: Review and suggestion for application[J]. Journal of Marketing Re- search, 1983, 20: 135-148.
  • 7Guha G, Rastogi R, Shim K. CURE: An efficient clustering algorithm for large databases[C]// Proceedings of ACM SIGMOD International Conference on Management of Data. ACM New York, NY, USA, 1998: 73-84.
  • 8Karypis G, Han E H, Kumar V. CHAMELEON: Hierarchical clustering algorithm using dynamic modeling[J]. Computer, 1999, 32(8): 68-75.
  • 9Yue S H, Wei M M, Wang J S, et al. General grid-clustering approach[J]. Pattern Recognition Letters, 2008, 29(9): 1372-1384.
  • 10Gelbard R, Carmeli A, Bittmann R M, et al. Cluster analysis using multi-algorithm voting in cross-cultural studies[J]. Expert Systems with Applications, 2009, 36(7): 10438-10446.

二级参考文献27

共引文献108

同被引文献31

引证文献3

二级引证文献8

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部