The objective of this article is to further the study of journal interdisciplinarity, or, more generally, knowledge integration at the level of individual articles. Interdisciplinarity is operationalized by the diversity of subject fields assigned to cited items in the article's reference list. Subject fields and subfields were obtained from the L euven‐ B udapest ( ECOOM ) subject‐classification scheme, while disciplinary diversity was measured taking variety, balance, and disparity into account. As diversity measure we use a H ill‐type true diversity in the sense of J ost and L einster‐ C obbold. The analysis is conducted in 3 steps. In the first part, the properties of this measure are discussed, and, on the basis of these properties it is shown that the measure has the potential to serve as an indicator of interdisciplinarity. In the second part the applicability of this indicator is shown using selected journals from several research fields ranging from mathematics to social sciences. Finally, the often‐heard argument, namely, that interdisciplinary research exhibits larger visibility and impact, is studied on the basis of these selected journals. Yet, as only 7 journals, representing a total of 15,757 articles, are studied, albeit chosen to cover a large range of interdisciplinarity, further research is still needed.

This article offers important background information about a new product, the Book Citation Index (BKCI), launched in 2011 by Thomson Reuters. Information is illustrated by some new facts concerning The BKCI's use in bibliometrics, coverage analysis, and a series of idiosyncrasies worthy of further discussion. The BKCI was launched primarily to assist researchers identify useful and relevant research that was previously invisible to them, owing to the lack of significant book content in citation indexes such as the Web of Science. So far, the content of 33,000 books has been added to the desktops of the global research community, the majority in the arts, humanities, and social sciences fields. Initial analyses of the data from The BKCI have indicated that The BKCI, in its current version, should not be used for bibliometric or evaluative purposes. The most significant limitations to this potential application are the high share of publications without address information, the inflation of publication counts, the lack of cumulative citation counts from different hierarchical levels, and inconsistency in citation counts between the cited reference search and the book citation index. However, The BKCI is a first step toward creating a reliable and necessary citation data source for monographs — a very challenging issue, because, unlike journals and conference proceedings, books have specific requirements, and several problems emerge not only in the context of subject classification, but also in their role as cited publications and in citing publications.

In this article, the authors answer the question of whether the field of scientometrics/bibliometrics shares essential characteristics of “metrics” sciences. To achieve this objective, the citation network of seven selected metrics and their information environment is analyzed.

Clustering by integrating multiview representations has become a crucial issue for knowledge discovery in heterogeneous environments. However, most prior approaches assume that the multiple representations share the same dimension, limiting their applicability to homogeneous environments. In this paper, we present a novel tensor-based framework for integrating heterogeneous multiview data in the context of spectral clustering. Our framework includes two novel formulations; that is multiview clustering based on the integration of the Frobenius-norm objective function (MC-FR-OI) and that based on matrix integration in the Frobenius-norm objective function (MC-FR-MI). We show that the solutions for both formulations can be computed by tensor decompositions. We evaluated our methods on synthetic data and two real-world data sets in comparison with baseline methods. Experimental results demonstrate that the proposed formulations are effective in integrating multiview data in heterogeneous environments.

A new visual representation of the response time, i.e., the time elapsed between the publication year and the date of the first citation of a paper, is provided. This presentation can be used to detect and describe different paradigmatic types of reception speed for scientific journals.

This paper presents a novel optimized kernel k-means algorithm (OKKC) to combine multiple data sources for clustering analysis. The algorithm uses an alternating minimization framework to optimize the cluster membership and kernel coefficients as a nonconvex problem. In the proposed algorithm, the problem to optimize the cluster membership and the problem to optimize the kernel coefficients are all based on the same Rayleigh quotient objective; therefore the proposed algorithm converges locally. OKKC has a simpler procedure and lower complexity than other algorithms proposed in the literature. Simulated and real-life data fusion applications are experimentally studied, and the results validate that the proposed algorithm has comparable performance, moreover, it is more efficient on large-scale data sets. (The Matlab implementation of OKKC algorithm is downloadable from http://homes.esat.kuleuven.be/~sistawww/bio/syu/okkc.html.).

MOTIVATION: We propose a novel algorithm to combine multiple kernels and Laplacians for clustering analysis. The new algorithm is formulated on a Rayleigh quotient objective function and is solved as a bi-level alternating minimization procedure. Using the proposed algorithm, the coefficients of kernels and Laplacians can be optimized automatically. RESULTS: Three variants of the algorithm are proposed. The performance is systematically validated on two real-life data fusion applications. The proposed Optimized Kernel Laplacian Clustering (OKLC) algorithms perform significantly better than other methods. Moreover, the coefficients of kernels and Laplacians optimized by OKLC show some correlation with the rank of performance of individual data source. Though in our evaluation the K values are predefined, in practical studies, the optimal cluster number can be consistently estimated from the eigenspectrum of the combined kernel Laplacian matrix. AVAILABILITY: The MATLAB code of algorithms implemented in this paper is downloadable from http://homes.esat.kuleuven.be/~sistawww/bioi/syu/oklc.html.

A bibliometric method for analyzing and visualizing national research profiles is adapted to describe national preferences for publishing particular document types. Similarities in national profiles and national peculiarities are discussed based on the publication output of the 26 most active countries indexed in the Web of Science annual volume 2007.

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.