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)
0.05
0.046299513 = product of:
0.09259903 = sum of:
0.09259903 = product of:
0.18519805 = sum of:
0.18519805 = weight(_text_:mining in 3464) [ClassicSimilarity], result of:
0.18519805 = score(doc=3464,freq=6.0), product of:
0.28585905 = queryWeight, product of:
5.642448 = idf(docFreq=425, maxDocs=44218)
0.05066224 = queryNorm
0.64786494 = fieldWeight in 3464, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
5.642448 = idf(docFreq=425, maxDocs=44218)
0.046875 = fieldNorm(doc=3464)
0.5 = coord(1/2)
0.5 = coord(1/2)
- 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.
- Theme
- Data Mining
