Does it make sense to apply clustering on aggregation of data?
Since some features are missing for specific sources, the missing values are not missing-at-random but are systematically missing. In this situation, I'd advise against doing clustering on the combined data set with all available features. If missing values were occurring at random, you could have used some missing value imputation method before performing cluster analysis. However, since the values are systematically missing, imputation would be difficult to tackle. (You could try to predict those missing values, but I am afraid that will add a lot of unnecessary noise in the data.)

I'd recommend choosing from one of these two options:


Perform clustering on the combined data set, but use only those features that are non-missing across all sources. 
Perform three different cluster analysis, one for each source. This way, you can ensure that you are using as many features (information) as possible. The determination of "abnormal" behavior can then be determined within each source. This can be an added benefit since it would allow you to be more specific about why a use might be abnormal, as you have more features that can be used to explain this. The results can also be then summarized across all sources to create one consolidated report.