Categorical Clustering of Users Reading Habits I have a data set with a set of users and a history of documents they have read, all the documents have metadata attributes (think topic, country, author) associated with them.

I want to cluster the users based on their reading history per one of the metadata attributes associated with the documents they have clicked on. This attribute has 7 possible categorical values and I want to prove a hypothesis that there is a pattern to the users' reading habits and they can be divided into seven clusters. In other words, that users will often read documents based on one of the 7 possible values in the particular metadata category.

Anyone have any advice on how to do this especially in R, like specific packages? I realize that the standard k-means algorithm won't work well in this case since the data is categorical and not numeric.
Sounds like you are set to do collaborative filtering - you have readers (users) and documents (items) creating a unary-response matrix with 1 cells indicating the reader (row) read the given document (col). There are various types of recommender systems (e.g., user-based and item-based collaborative filters) and several of them can account for the meta-data associated with your documents (e.g., content-based systems). You should look into R package {recommenderlab}