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.

To obtain correlated and complementary information contained in text mining and bibliometrics, hybrid clustering to incorporate textual content and citation information has become a popular strategy. In this paper, we propose a new computational framework of integrating text mining and bibliometrics to provide a mapping of journal sets. Two different approaches of hybrid clustering methods are applied in this paper. The first category is ensemble clustering, which combines different clustering results obtained from individual data into a consolidated clustering result. The second category is kernel fusion, which maps heterogeneous data sets into the kernel space and combines the kernel matrices for clustering. Kernels can be combined either averagely, or by an optimized weighted linear combination model. In this paper, we propose a novel adaptive kernel K-means clustering algorithm to combine textual content and citation information for clustering. The proposed algorithm is systematically compared with other methods on a clustering problem of 1869 journals published in 2002–2006. Based on several validation indices, the experimental results demonstrate that our hybrid clustering strategy is able to provide clustering result as well as the best individual data source.

To unravel the concept structure and dynamics of the bioinformatics field, we analyze a set of 7401 publications from the Web of Science and MEDLINE databases, publication years 1981–2004. For delineating this complex, interdisciplinary field, a novel bibliometric retrieval strategy is used. Given that the performance of unsupervised clustering and classification of scientific publications is significantly improved by deeply merging textual contents with the structure of the citation graph, we proceed with a hybrid clustering method based on Fisher’s inverse chi-square. The optimal number of clusters is determined by a compound semi-automatic strategy comprising a combination of distance-based and stability-based methods. We also investigate the relationship between number of Latent Semantic Indexing factors, number of clusters, and clustering performance. The HITS and PageRank algorithms are used to determine representative publications in each cluster. Next, we develop a methodology for dynamic hybrid clustering of evolving bibliographic data sets. The same clustering methodology is applied to consecutive periods defined by time windows on the set, and in a subsequent phase chains are formed by matching and tracking clusters through time. Term networks for the eleven resulting cluster chains present the cognitive structure of the field. Finally, we provide a view on how much attention the bioinformatics community has devoted to the different subfields through time.

Microarray classification can be useful to support clinical management decisions for individual patients in, for example, oncology. However, comparing classifiers and selecting the best for each microarray dataset can be a tedious and non-straightforward task. The M@CBETH (a MicroArray Classification BEnchmarking Tool on a Host server) web service offers the microarray community a simple tool for making optimal two-class predictions. M@CBETH aims at finding the best prediction among different classification methods by using randomizations of the benchmarking dataset. The M@CBETH web service intends to introduce an optimal use of clinical microarray data classification.