A hierarchical and optimal clustering of WoS journal database by hybrid information

Proceedings of ISSI 2011 - 13th Conference of the International Society for Scientometrics and Informetrics

Volumn 1, Issue , 2011, Pages 485-496

  Liu, Xinhai ^a,b   Glänzel, Wolfgang ^a,c   de Moor, Bart ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b WUHAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^c INSTITUTE OF EXPERIMENTAL MEDICINE   (Hungary)

Author keywords

[No Author keywords available]

Indexed keywords

DATABASE SYSTEMS; VECTOR SPACES;

CITATION INFORMATION; CLUSTERING ACCURACY; COGNITIVE ANALYSIS; HIERARCHICAL ANALYSIS; INTEGRATED ANALYSIS; LINEAR COMBINATIONS; OPTIMAL CLUSTERING; VECTOR SPACE MODELS;

HIERARCHICAL CLUSTERING;

EID: 84862792487     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (31)

1. Baeza-Yates, R. & Ribeiro-Neto, B. (1999). Modern information retrieval. Cambridge: Addison-Wesley.
2. Batagelj, V. & Mrvar, A. (2002). Pajek - Analysis and visualization of large networks. Graph Drawing, 2265, 477-478.
3. Blondel, V. D., Guillaume,J.-L., Lambiotte, R. and E. Lefebvre. (2008) Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment. 10, 10008.
4. Braam, R.R., Moed, H.F. & van Raan, A.F.J. (1991a). Mapping of science by combined cocitation and word analysis, Part 1: Structural aspects. Journal of the American Society for Information Science, 42 (4), 233–251.
5. Braam, R. R., Moed, H. F. & van Raan, A. F. J. (1991b). Mapping of Science by Combined Cocitation and Word Analysis. 2. Dynamic Aspects. JASIS, 42, 252-266.
6. Calado, P., Ribeiro-Neto, B., Ziviani, N., Moura, E. & Silva, I. (2003). Local versus global link information in the Web. ACM Transactions on Information Systems, 21, 42-63.
7. Calado, P., Cristo, M., Goncalves, M. A., de Moura, E. S., Ribeiro-Neto, B. & Ziviani, N. (2006). Link-based similarity measures for the classification of Web documents. JASIST, 57, 208-221.
8. Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486, 75-174.
9. Glenisson, P., Glänzel, W., Janssens, F. & De Moor, B. (2005). Combining full text and bibliometric information in mapping scientific disciplines. Information Processing & Management, 41 (6), 1548-1572.
10. Hatcher, E. & Gospodnetić, O. (2004). Lucene in Action. New York: Manning Publications Co.
11. He, X.; Ding, C. H. Q.; Zha, H. & Simon, H. D.(2001) Automatic Topic Identification Using Webpage Clustering Proceedings of the 2001 IEEE International Conference on Data Mining.
12. Hedges, L.V. & Olkin, I. (1985). Statistical methods for meta-analysis. San Diego: Academic Press.
13. Hubert, L & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2 (1), 193–218.
14. Jaccard, P. (1901) Étude comparative de la distribution florale dans une portion des Alpes et des Jura. Bulletin de la Société Vaudoise des Sciences Naturelles, 37, 547–579.
15. Jain, A. & Dubes, R. (1988). Algorithms for clustering data. New Jersey: Prentice Hall.
16. Jain, A. K. (2001). Data clustering: 50 years beyond k-means. Pattern Recognition Letters, 31(8), 651-666.
17. Janssens, F., Leta, J., Glänzel, W. & De Moor, B. (2006a). Towards mapping library and information science. Information Processing & Management, 42(6), 1614–1642.
18. Janssens, F., Tran Quoc, V., Glänzel, W. & De Moor, B. (2006b). Integration of textual content and link information for accurate clustering of science fields. In V. P. Guerrero-Bote (Ed.), Proc. of the I Intl. Conf. on Multidisciplinary Information Sciences and Technologies (InSciT2006) (pp. 615-619), Mérida, Spain.
19. Janssens, F., Glänzel, W., & De Moor, B. (2008). A hybrid mapping of information science. Scientometrics, 75(3), 607-631.
20. Janssens F., Zhang L., De Moor B., & Glänzel W. (2009). Hybrid clustering for validation and improvement of subject-classification schemes. Information Processing & Management, 45 (6), 683-702.
21. Joachims, T., Cristianini, N. & Shawe-Taylor, J. (2001). Composite kernels for hypertext categorisation. In Proceedings of the 18th International Conference on Machine Learning (ICML) (pp. 250-257).
22. Lambiotte, R. & Panzarasa, P. Communities (2009). Knowledge Creation, and Information Diffusion. Journal of Informetrics, 3 (3), 180-190
23. Modha, D. S. & Spangler, W. S. (2000). Clustering hypertext with applications to web searching. ACM Conference on Hypertext (pp. 143-152).
24. Mullins, N., Snizek, W. & Oehler, K. (1988). The structural analysis of a scientific paper. In A. F. J. vanRaan (Ed.), Handbook of quantitative studies of science and technology (pp. 81–105). New York: Elsevier Science.
25. Newman, M. (2004). Detecting community structure in networks. Eur. Phys. J. B, 38, 321–330.
26. Rafols, I. & Leydesdorff, L. (2009). Content-based and Algorithmic Classifications of Journals: Perspectives on the Dynamics of Scientific Communication and Indexer Effects. J. Am. Soc. Inf. Sci. Technol., 60(9), 1532-2882
27. Salton, G. & McGill, M. J. (1986). Introduction to modern information retrieval. New York: McGraw-Hill, Inc.
28. Strehl, A., & Ghosh, J. (2002). Cluster Ensembles-A Knowledge Reuse Framework for Combining Multiple Partitions. Journal of Machine Learning Research, 3, 583-617.
29. Snizek, W., Oehler, K. & Mullins, N. (1991). Textual and nontextual characteristics of scientific papers: Neglected science indicators. Scientometrics, 20 (1), 25-35.
30. Wang, Y. & Kitsuregawa, M. (2002). Evaluating contents-link coupled web page clustering for web search results. In Proc. of the 11th intl. conf. on Information and knowledge management (CIKM) (pp. 499-506).
31. Zhang L., Liu X.H., Janssens F., Liang L & Glänzel W. (2010). Subject clustering analysis based on ISI category classification. Journal of Informetrics, 4 (2), 185-193.