  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 ten 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.

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