# When does it makes senses to use Dot-Product as similarity measure instead of Cosine?

When does it makes senses to use Dot-Product as similarity measure instead of Cosine?. I have seen there is already question asked about this, However that merely explains the difference between calculation of dot-product & cosine and it does not focus on when should we use one vs another with real world example.

When we want cluster items use distances as similarity measure. For example, we use Euclidean distances(square root of inner product) in k-means clustering as a similarity measure. Squared Euclidean distances are used as a similarity measure in Ward's method of clustering. However, when we want to cluster variables, we use correlation(cosine) as a measure of similarity.