Clustering with 0 or Null values I want to do some clustering for a dataset where I am looking at 10,000 peoples usage of certain electronic devices. I have 11 columns; the first column is simply a URN representing each person in the study. Then the other ten columns are for the weekly usage, in minutes, of each device (e.g. phone, laptop, t.v. etc). 

No individual actually has all ten devices. They normally tend to have 2-4. For this reason they will have a value of n for each of the devices they use, and then a "0" if they do not have said device. Therefore, I obviously can't omit rows which have a 0 in them, and the 0 does not necessarily mean missing data, as opposed to actually meaning they do not own the device. 

I am wondering how a typical basic cluster analysis would be carried out in a situation like this? / what would be an appropriate way to approach such a study? (I am using R for the analysis, for reference) 

Thanks for the help.
K-means is the wrong algorithm for this problem.

It assumes dense, continuous input data, which you haven't.