I am starting to play around in datamining / machine learning and I am stuck on a problem that's probably easy.So I have a report that lists the url and the number of visits a person did. So a combination of ip and url result in an amount of visits.Now I want to run the k-means clustering algorithm on this so I thought I could approach it like this:This is my data:url      ip    visitsabc.be   123   5abc.be/a 123   2abc.be/b 123   2abc.be/b 321   4And I would turn in into a feature vector/matrix like so:abc.be  abc.be/a   abc.be/b   impressions   1       0          0          5   0       1          0          2   0       0          1          2   0       0          1          4But I am stuck on how to transform my data set to a feature matrix. Any help would be appreciated.
Going from report to feature matrix
feature extraction;k means
I don't understand what you mean by So I have a report that lists the url and the number of visits a person did. So a combination of ip and url result in an amount of visits.Assuming that you equate an IP with a user, and you wish to cluster users by their URL visitation frequencies, your matrix, M, would have One row per IP (user)One column for each URL that you are tracking (your features)and the entries in M would be visits of a given URL by a particular IPGiven these assumptions, and your report, M would be:    abc.be  abc.be/a  abc.be/b123   5        2         2321   0        0         4