Why do we need algorithmic historiography?
Journal of the American Society for Information Science and Technology
Identifying a better measure of relatedness for mapping science
Journal of the American Society for Information Science and Technology
Journal of the American Society for Information Science and Technology
A new approach for detecting scientific specialties from raw cocitation networks
Journal of the American Society for Information Science and Technology
Toward a consensus map of science
Journal of the American Society for Information Science and Technology
Comparative study on methods of detecting research fronts using different types of citation
Journal of the American Society for Information Science and Technology
Hybrid clustering for validation and improvement of subject-classification schemes
Information Processing and Management: an International Journal
Journal of the American Society for Information Science and Technology
Journal of the American Society for Information Science and Technology
A comparison of two techniques for bibliometric mapping: Multidimensional scaling and VOS
Journal of the American Society for Information Science and Technology
Multilevel local search algorithms for modularity clustering
Journal of Experimental Algorithmics (JEA)
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Classifying journals or publications into research areas is an essential element of many bibliometric analyses. Classification usually takes place at the level of journals, where the Web of Science subject categories are the most popular classification system. However, journal-level classification systems have two important limitations: They offer only a limited amount of detail, and they have difficulties with multidisciplinary journals. To avoid these limitations, we introduce a new methodology for constructing classification systems at the level of individual publications. In the proposed methodology, publications are clustered into research areas based on citation relations. The methodology is able to deal with very large numbers of publications. We present an application in which a classification system is produced that includes almost 10 million publications. Based on an extensive analysis of this classification system, we discuss the strengths and the limitations of the proposed methodology. Important strengths are the transparency and relative simplicity of the methodology and its fairly modest computing and memory requirements. The main limitation of the methodology is its exclusive reliance on direct citation relations between publications. The accuracy of the methodology can probably be increased by also taking into account other types of relations–for instance, based on bibliographic coupling. © 2012 Wiley Periodicals, Inc.