Algorithms for clustering data
Algorithms for clustering data
Clustering hypertext with applications to web searching
HYPERTEXT '00 Proceedings of the eleventh ACM on Hypertext and hypermedia
Modern Information Retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Evaluating contents-link coupled web page clustering for web search results
Proceedings of the eleventh international conference on Information and knowledge management
Local versus global link information in the Web
ACM Transactions on Information Systems (TOIS)
Composite Kernels for Hypertext Categorisation
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Lucene in Action (In Action series)
Lucene in Action (In Action series)
Link-based similarity measures for the classification of Web documents
Journal of the American Society for Information Science and Technology
Towards mapping library and information science
Information Processing and Management: an International Journal - Special issue: Informetrics
A tutorial on spectral clustering
Statistics and Computing
A global map of science based on the ISI subject categories
Journal of the American Society for Information Science and Technology
Hybrid clustering for validation and improvement of subject-classification schemes
Information Processing and Management: an International Journal
Combining full text and bibliometric information in mapping scientific disciplines
Information Processing and Management: an International Journal - Special issue: Infometrics
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Web document clustering using hyperlink structures
Computational Statistics & Data Analysis
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Previous studies have shown that hybrid clustering methods based on textual and citation information outperforms clustering methods that use only one of these components. However, former methods focus on the vector space model. In this paper we apply a hybrid clustering method which is based on the graph model to map the Web of Science database in the mirror of the journals covered by the database. Compared with former hybrid clustering strategies, our method is very fast and even achieves better clustering accuracy. In addition, it detects the number of clusters automatically and provides a top-down hierarchical analysis, which fits in with the practical application. We quantitatively and qualitatively asses the added value of such an integrated analysis and we investigate whether the clustering outcome provides an appropriate representation of the field structure by comparing with a text-only or citation-only clustering and with another hybrid method based on linear combination of distance matrices. Our dataset consists of about 8,000 journals published in the period 2002---2006. The cognitive analysis, including the ranked journals, term annotation and the visualization of cluster structure demonstrates the efficiency of our strategy.