Similarity Measures, Author Cocitation Analysis, and Information Theory. Journal of the American Society for Information Science & Technology JASIST 56(7), 2005, 769-772.

Persistent Link:
http://hdl.handle.net/10150/105170
Title:
Similarity Measures, Author Cocitation Analysis, and Information Theory. Journal of the American Society for Information Science & Technology JASIST 56(7), 2005, 769-772.
Author:
Leydesdorff, Loet
Citation:
Similarity Measures, Author Cocitation Analysis, and Information Theory. Journal of the American Society for Information Science & Technology JASIST 56(7), 2005, 769-772. 2005,
Issue Date:
2005
URI:
http://hdl.handle.net/10150/105170
Submitted date:
2006-10-25
Abstract:
The use of Pearsonâ s correlation coefficient in Author Cocitation Analysis was compared with Saltonâ 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.
Type:
Preprint
Language:
en
Keywords:
Science Technology Studies

Full metadata record

DC FieldValue Language
dc.contributor.authorLeydesdorff, Loeten_US
dc.date.accessioned2006-10-25T00:00:01Z-
dc.date.available2010-06-18T23:20:33Z-
dc.date.issued2005en_US
dc.date.submitted2006-10-25en_US
dc.identifier.citationSimilarity Measures, Author Cocitation Analysis, and Information Theory. Journal of the American Society for Information Science & Technology JASIST 56(7), 2005, 769-772. 2005,en_US
dc.identifier.urihttp://hdl.handle.net/10150/105170-
dc.description.abstractThe use of Pearsonâ s correlation coefficient in Author Cocitation Analysis was compared with Saltonâ 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.en_US
dc.format.mimetypehtmen_US
dc.language.isoenen_US
dc.subjectScience Technology Studiesen_US
dc.titleSimilarity Measures, Author Cocitation Analysis, and Information Theory. Journal of the American Society for Information Science & Technology JASIST 56(7), 2005, 769-772.en_US
dc.typePreprinten_US
All Items in UA Campus Repository are protected by copyright, with all rights reserved, unless otherwise indicated.