Weighted Hybrid Clustering by Combining Text Mining and Bibliometrics on a Large-Scale Journal Database

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