Editing Distance matrix (section)

=== Evaluation of the similarity or dissimilarity of Cosine similarity and Distance matrices ===
[[File:SimilarityTOidistance.png|none|thumb|Conversion formula between cosine similarity and Euclidean distance]]

*[https://www.sciencedirect.com/science/article/pii/S0020025507002630]* While the [[Cosine similarity]] measure is perhaps the most frequently applied proximity measure in information retrieval by measuring the angles between documents in the search space on the base of the cosine. Euclidean distance is invariant to mean-correction. The sampling distribution of a mean is generated by repeated sampling from the same population and recording of the sample means obtained. This forms a distribution of different means, and this distribution has its own mean and variance. For the data which can be negative as well as positive, the [[null distribution]] for cosine similarity is the distribution of the [[dot product]] of two independent random unit vectors. This distribution has a mean of zero and a variance of 1/n. While [[Euclidean distance]] will be invariant to this correction.