Computer Science – Information Retrieval
Scientific paper
2009-11-06
Journal of the American Society for Information Science and Technology 60(5) (2009) 1027-1036
Computer Science
Information Retrieval
Scientific paper
The relation between Pearson's correlation coefficient and Salton's cosine measure is revealed based on the different possible values of the division of the L1-norm and the L2-norm of a vector. These different values yield a sheaf of increasingly straight lines which form together a cloud of points, being the investigated relation. The theoretical results are tested against the author co-citation relations among 24 informetricians for whom two matrices can be constructed, based on co-citations: the asymmetric occurrence matrix and the symmetric co-citation matrix. Both examples completely confirm the theoretical results. The results enable us to specify an algorithm which provides a threshold value for the cosine above which none of the corresponding Pearson correlations would be negative. Using this threshold value can be expected to optimize the visualization of the vector space.
Egghe Leo
Leydesdorff Loet
No associations
LandOfFree
The relation between Pearson's correlation coefficient r and Salton's cosine measure does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with The relation between Pearson's correlation coefficient r and Salton's cosine measure, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and The relation between Pearson's correlation coefficient r and Salton's cosine measure will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-391822