Subject clustering analysis based on ISI category classification

The study focuses on the analysis of the information flow among the ISI subject categories and aims at finding an appropriate field structure of the Web of Science using the subject clustering algorithm developed in previous studies. The elaborate clustering of more than 8,000 journals and the clustering of the ISI subject categories provide two subject classification schemes through different perspectives and levels. The two clustering results have been compared and the according accordance and divergence have been analyzed. Several indicators have been used to compare the communication characteristics among different ISI subject categories. The neighbour map of each category clearly reflects the affinities between the “core” category and its satellites around. Introduction A series of previous studies focused on the analyses of journal clustering based on a complete journal-journal cross-citation matrix (Zhang et al, 2009; Janssens et al, 2009; Zhang et al, 2009). The Institute of Scientific Information (ISI) has assigned each journal included to one or more subject categories. Based on this classification scheme, the journal-journal matrix can be aggregated to a category-category matrix, which is much more densely populated than that on the journal level. The present study will focus on the analysis of the information flow among the ISI subject categories. This will be done by two important reasons. This exercise aims at finding an appropriate field structure of the Web of Science using the subject clustering algorithm developed in previous studies. Furthermore, since ISI Subject Categories are based on journal assignment the question arises of whether what changes if journal crosscitation is replaced by subject cross-citation. If changes are not essential, the elaborate clustering of more than 8,000 journals could be substituted by a somewhat easier analysis of roughly 250 ISI categories and the journal level could, as it were, be skipped. However, we * Corresponding author stress that cross-citations are calculated from individual paper-to-paper links whatever aggregation levels are chosen. The other reason is to analyze whether multiple journal assignment to subject categories interferes with, distorts or even determines the resulting cluster structure. Before we introduce the methodological rudiments, we briefly summarise the historical background and the outcomes of previous or related studies. Along with the development of computerised scientometrics, mapping of science plays an important role in the construction, learning, and dissemination of science structure. For instance, a variety of techniques for analyzing journal-journal citation relationships have been reported in the literatures to cluster scientific journals (Doreian and Fararo, 1985; Tijssen et al., 1987; Leydesdorff, 2006). An alternative method of co-citation clustering has been investigated in constructing a World Atlas of Sciences for ISI (Garfield et al., 1975; Leydesdorff, 1987; Small, 1999). Boyack, Klavans, and Borner (2005) applied eight alternative measures of journal similarity to a dataset of 7,121 journals covering over one million documents in the combined Science Citation and Social Sciences Citation Indexes, to show a global map of science using the force-directed graph layout tool VxOrd. Chen (2008) proposes an approach to classify scientific networks in terms of aggregated journal-journal citation relations of the ISI Journal Citation Reports using the affinity propagation method. As mentioned in the outset, Zhang et al. (2009) have also investigated different methods for the analysis and classifications of scientific journals. Besides using journals as the units of analysis, some recent researches focus on the science structure based on the subject categories. Glänzel and Schubert (2003) designed a new classification scheme of science fields and subfields for scientometric evaluation purposes. Moya-Anegon et al. (2004) proposed a new technique that uses thematic classification as entities of co-citation, and presented an egocentered network of 222 ISI categories including science and social sciences. Leydesdorff and Rafols (2009) classified the ISI 172 science categories into 14 groups based on factor analysis, and compared the interdisciplinarity of each category using betweenness centrality. Compared to other researchers, we applied a new clustering technique to classify the ISI science and social sciences categories into 7 groups based on the category-category cross-citation similarities, and further compared the results with the 7 hybrid clustering solution of 8305 journals in a previous study (Zhang et al, 2009). Furthermore, several indicators have been used to analyze the communication characteristics of different categories. Data sources and processing The data has been collected from the Web of Science of Thomson-Reuters (Philadelphia, PA, USA). Altogether 9487 journals which were assigned to the 246 categories of sciences, social sciences and arts and humanities in the entire period of 2002-2006 were selected and only four document types, namely, article, note, letter and review, were taken into consideration. More than six million papers were indexed and citations have been summed up through a variable citation window, from the publication year till 2006.