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
2 × 2 tables are encountered in various scientific disciplines, including biomedical, social and beh...
Abstract—The measure of similarity normally utilized in statistical signal processing is based on se...
Linfoot (1957) introduced an informational measure rI of correlation between two random variables X ...
textabstractWe provide a number of new insights into the methodological discussion about author coci...
We provide a number of new insights into the methodological discussion about author cocitation analy...
To study concepts that are coded in language, researchers often collect lists of conceptual properti...
The relation between Pearson's correlation coefficient and Salton's cosine measure is revealed based...
Pearson\u27s r has been used as similarity measure in author co-citation analysis since the introduc...
textabstractIn scientometric research, the use of co-occurrence data is very common. In many cases, ...
The debate about which similarity measure one should use for the normalization in the case of Author...
This study aims to present and analyze Pearson Correlation Coefficient (r) and Salton's Cosine (CS) ...
Comparing textual content is becoming more and more problematic due to the fact that nowadays data i...
Co-occurrence matrices, such as cocitation, coword, and colink matrices, have been used widely in th...
Abstract Text similarity measurement aims to find the commonality existing among text documents, whi...
We prove that Ochiai similarity of the co-occurrence matrix is equal to cosine similarity in the und...
2 × 2 tables are encountered in various scientific disciplines, including biomedical, social and beh...
Abstract—The measure of similarity normally utilized in statistical signal processing is based on se...
Linfoot (1957) introduced an informational measure rI of correlation between two random variables X ...
textabstractWe provide a number of new insights into the methodological discussion about author coci...
We provide a number of new insights into the methodological discussion about author cocitation analy...
To study concepts that are coded in language, researchers often collect lists of conceptual properti...
The relation between Pearson's correlation coefficient and Salton's cosine measure is revealed based...
Pearson\u27s r has been used as similarity measure in author co-citation analysis since the introduc...
textabstractIn scientometric research, the use of co-occurrence data is very common. In many cases, ...
The debate about which similarity measure one should use for the normalization in the case of Author...
This study aims to present and analyze Pearson Correlation Coefficient (r) and Salton's Cosine (CS) ...
Comparing textual content is becoming more and more problematic due to the fact that nowadays data i...
Co-occurrence matrices, such as cocitation, coword, and colink matrices, have been used widely in th...
Abstract Text similarity measurement aims to find the commonality existing among text documents, whi...
We prove that Ochiai similarity of the co-occurrence matrix is equal to cosine similarity in the und...
2 × 2 tables are encountered in various scientific disciplines, including biomedical, social and beh...
Abstract—The measure of similarity normally utilized in statistical signal processing is based on se...
Linfoot (1957) introduced an informational measure rI of correlation between two random variables X ...