I have a 10 dimensional space which contain points that contain a 1 or 0 .
example of two points :
point1 : 1,1,1,0,0,0,1,1,0,1 point2 : 1,0,1,0,0,0,1,0,0,0
Which distance function should I use for this. I've been trying Euclidean and Manhattan but I don't which one has an advantage over the other ?
This is binary data (1=present, 0=absent) to indicate what links are associated with a user.
For example the first binary digit might indicate that a user has www.google.com configured or not.
The clusters of links will each contain users links. For each cluster I want to recommend any link which one user has added but the other has not, this is just for users within the same cluster. So I guess this is a recommendation system based on k-means clustering.