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Weighted Hybrid Clustering by Combining Text Mining and Bibliometrics on a Large-Scale Journal Database (2009)
| Citations: | 7 - 2 self |
BibTeX
@MISC{Liu09weightedhybrid,
author = {Xinhai Liu and Shi Yu and Frizo Janssens and Wolfgang Glänzel and Yves Moreau and Bart De Moor},
title = {Weighted Hybrid Clustering by Combining Text Mining and Bibliometrics on a Large-Scale Journal Database},
year = {2009}
}
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Abstract
We propose a new hybrid clustering framework to incor-porate text mining with bibliometrics in journal set anal-ysis.The framework integrates two different approaches: clustering ensemble and kernel-fusion clustering. To improve the flexibility and the efficiency of process-ing large-scale data, we propose an information-based weighting scheme to leverage the effect of multiple data sources in hybrid clustering. Three different algorithms are extended by the proposed weighting scheme and they are employed on a large journal set retrieved from the Web of Science (WoS) database. The clustering per-formance of the proposed algorithms is systematically evaluated using multiple evaluation methods, and they were cross-compared with alternative methods. Experi-mental results demonstrate that the proposed weighted hybrid clustering strategy is superior to other meth-ods in clustering performance and efficiency. The pro-posed approach also provides a more refined structural mapping of journal sets, which is useful for monitoring and detecting new trends in different scientific fields.
Keyphrases
hybrid clustering text mining large-scale journal database new trend alternative method information-based weighting scheme process-ing large-scale data incor-porate text mining different approach kernel-fusion clustering large journal multiple evaluation method pro-posed approach different scientific field weighting scheme multiple data source different algorithm journal set experi-mental result refined structural mapping journal set anal-ysis
