Liu, X.; Yu, S.; Janssens, F.; Glänzel, W.; Moreau, Y.; Moor, B.de: Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database (2010)
0.00
0.0039320644 = product of:
0.015728258 = sum of:
0.015728258 = weight(_text_:information in 3464) [ClassicSimilarity], result of:
0.015728258 = score(doc=3464,freq=4.0), product of:
0.09556833 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.054440062 = queryNorm
0.16457605 = fieldWeight in 3464, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046875 = fieldNorm(doc=3464)
0.25 = coord(1/4)
- Abstract
- We propose a new hybrid clustering framework to incorporate text mining with bibliometrics in journal set analysis. The framework integrates two different approaches: clustering ensemble and kernel-fusion clustering. To improve the flexibility and the efficiency of processing 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 performance of the proposed algorithms is systematically evaluated using multiple evaluation methods, and they were cross-compared with alternative methods. Experimental results demonstrate that the proposed weighted hybrid clustering strategy is superior to other methods in clustering performance and efficiency. The proposed approach also provides a more refined structural mapping of journal sets, which is useful for monitoring and detecting new trends in different scientific fields.
- Source
- Journal of the American Society for Information Science and Technology. 61(2010) no.6, S.1105-1119