Similarity measures, author cocitation analysis, and information theory

Loet Leydesdorff A

123 citations0/0 files storedJournal of the American Society for Information Science and Technology

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scientometrics and bibliometrics research Library Science and Information Web visibility and informetrics

Abstract

Abstract The use of Pearson's correlation coefficient in Author Cocitation Analysis was compared with Salton's cosine measure in a number of recent contributions. Unlike the Pearson correlation, the cosine is insensitive to the number of zeros. However, one has the option of applying a logarithmic transformation in correlation analysis. Information calculus is based on both the logarithmic transformation and provides a non‐parametric statistics. Using this methodology, one can cluster a document set in a precise way and express the differences in terms of bits of information. The algorithm is explained and used on the data set, which was made the subject of this discussion.

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References

1986Mapping the Dynamics of Science and Technology 2003Requirements for a cocitation similarity measure, with special reference to Pearson's correlation coefficient 1985Clustering thescience citation index ® using co-citations 2020The Challenge of Scientometrics: The Development, Measurement, and Self-Organization of Scientific Communications 1985Clustering the science citation index using co-citations. II. Mapping science 1987Various methods for the mapping of science 2003Author cocitation analysis and Pearson's r 2001The Challenge of Scientometrics: The Development, Measurement, and Self-Organization of Scientific Communications, pp. 1-25

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