数字图书馆中的检索结果聚类和关联推荐研究被引量：13

Research on Retrieval Results Clustering and Relevant Recommendation in Digital Library

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摘要探讨如何在数字图书馆的文献检索平台中集成实现检索结果聚类、相关文献的关联推荐、相关作者和研究机构的关联推荐以及相关词语的关联推荐,由此帮助用户全面提高查准率和查全率,并且对聚类和推荐结果采用图形进行可视化展示,进一步提高用户的使用满意度。 This paper discusses how to implement the clustering of document retrieval results, the relevant recommendation of related documents, related authors ＆ organizations and related key - words. The author presents how to visualize the results of clustering and recommendation in graph on the documents retrieval platform of digital library, which can increase the users＇ satisfaction.

作者吉雍慧

机构地区南京大学信息管理系

出处《现代图书情报技术》 CSSCI 北大核心 2008年第2期69-75,共7页 New Technology of Library and Information Service

关键词数字图书馆文献检索聚类关联推荐可视化 GDI+ K-MEANS算法 Digital library Documents retrieval Clustering Relevant recommendation Visualization GDI ＋K - Means algorithm

分类号 G250.7 [文化科学—图书馆学]

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参考文献13

1Vivisimo. Inc - Enterprise Search, Federated Search and Clustering[ EB/OL]. [ 2007 - 11 - 08 ]. http://Vivislmo. com.
2Computer and Information Science Papers CiteSeer Publications Research Index[ EB/OL]. [2007 - 11 -08 ]. http://citeseer. ist. psu. edu.
3CNKI知识网络知识服务平台[EB/OL].[2007-11-08].http ://dlib. cnki. net/kns50/index. aspx.
4C J van Rijsbergen. Information Retrieval [ M ]. Second edition. London: Buttersworth, 1989.
5Sahon G, Wong A, Yang C S. A Vector Space Model for Automatic Indexing[J]. Communications of the ACM, 1975(18) :613 - 620.
6Jain A K, Murty M N, Flynn P J. Data Clustering: A Review[ J]. ACM Computing Surveys, 1999,31 ( 3 ) , 264 - 323.
7行小帅,潘进,焦李成.基于免疫规划的K-means聚类算法[J].计算机学报,2003,26(5):605-610. 被引量：81
8刘远超,王晓龙,刘秉权.一种改进的k-means文档聚类初值选择算法[J].高技术通讯,2006,16(1):11-15. 被引量：23
9万小军,杨建武,陈晓鸥.文档聚类中k-means算法的一种改进算法[J].计算机工程,2003,29(2):102-103. 被引量：29
10马文峰,高凤荣,王珊.论数字图书馆个性化信息推荐系统[J].现代图书情报技术,2003(2):16-18. 被引量：53

二级参考文献34

1Hatzivassiloglou V, Klavans J L, Holcombe M L, et al.Simfinder: A flexible clustering tool for surmnarization. In: Proceedings of the NAACI, 2001 Workshop on Automatic Surrunarization, Pittsburgh, PA, 2001, 41-49 .
2Jain A K,Dubes R C. Algorithms for clustering data. Englewood Cliffs NJ, USA: Prentice Hall, 1988.
3Sneath P H, Sokal R R. Numerical Taxonomy. London, UK:Freeman. 1973.
4King B. Step-wise clustering procedures. Journal of the Amercian Statistical Association , 1967, 69(8) :86-101.
5Guha S, Rastogi R, Shim K. CURE: An efficient clustering algorithm for large databases. Information Systems, 2001, 26( 1 ) : 35-58.
6Guha S, Rastogi R, Shim K. ROCK: a robust clustering algorithm for categorical attributes. In : Proceedings of the 15th International Cotfference on Data Engineering. Sydney: IEEE Computer Society Press, 1999. 512-521.
7Karypis G, Han E H, Kumar V. Chameleon: A hierarchical clustering algorithm using dynamic modeling. IEEE Computer, 1999, 32(8) :68-75.
8Han E H, Karypis G,Kumar V, et al. Clustering based on association rule hypergraphs. In: 1997 SIG-MOD Workshop on Research Issues on Data Mining and Knowledge Discovery, Tucson, Arizona, USA, 1997. 9-13.
9MacQueen J B. Some methods for classification and analysis of multivariate observations. In : Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley: University of California Press, 1967. 281-297.
10Yunjae J . Design and evaluation of clustering criterion for optimal hierarchicalagglomerative clustering:[PhD. Thesis].Minneapolis, Minnesota, USA: Department of Computer Science, University of Minnesota, 2001.