Profiling what is normal with categorical data I need some pointers / suggestions ... I like to profile a whole bunch of categorical user data - i.e. things like who talks to who, who deals with what product categories typically, and identify ones that don't follow a norm. An anomaly detection problem perhaps ...  but what sort of algos should I be looking at? Should I be hot encoding my fields to achieve this? 
All the examples I read (k means, one class SVM) are focused on numerical datasets ... 
 A: It depends entirely upon the meaning of your data attributes, which is left out in your question. You should provide more information about the data if you want more detailed help.
Whether a certain encoding "works" or not depends entirely upon the situation at hand, so you will have to reason about what you are trying to find out. You may even have to use different transformations for different attributes. 
For example, if there are 10 "product categories" and we encode them into numbers [1, 2, ..., 10], then a numerical anomaly detection method based on Euclidean distance will say that product 1 is "farther" away from product 10 than product 2 is, which may or may not be nonsensical. 
There is plenty of reading on categorical clustering available, for example, search for "categorical k-means", and you will probably find problems similar to yours. If you prefer books, Aggarwal's recent book on outlier detection (www.springer.com/us/book/9781461463955) has a chapter on categorical variables.
