Similarity Measures, Author Cocitation Analysis, and Information Theory

Details Similarity Measures, Author Cocitation Analysis, and Information Theory Similarity Measures, Author Cocitation Analysis, and Information Theory

2009-11-22

arxiv.org/abs/0911.4292v1

Journal of the American Society for Information Science & Technology, 56(7), 2005, 769-772

Computer Science

Information Retrieval

Scientific paper

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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