How to do a 'beer and diapers' correlation analysis I have data that is equivalent to:
shopper_1 = ['beer', 'eggs', 'water',...]
shopper_2 = ['diapers', 'beer',...]
...

I would like to do some analysis on this data set to get a correlation matrix that would have an implication similar to: if you bought x, you are likely to buy y.
Using python (or perhaps anything but MATLAB), how can I go about that? Some basic guidelines, or pointers to where I should look would help.
Thank you,
Edit - What I have learned: 


*

*These kinds of problems are known as association rule discovery. Wikipedia has a good article covering some of the common algorithms to do so. The classic algorithm to do so seems to be Apriori, due Agrawal et. al.

*That lead me to orange, a python interfaced data mining package. For Linux, the best way to install it seems to be from source using the supplied setup.py

*Orange by default reads input from files, formatted in one of several supported ways.

*Finally, a simple Apriori association rule learning is simple in orange.
 A: In addition to the links that were given in comments, here are some further pointers:


*

*Association rules and frequent itemsets

*Survey on Frequent Pattern Mining -- look around Table 1, p. 4


About Python, I guess now you have an idea of what you should be looking for, but the Orange data mining package features a package on Association rules and Itemsets (although for the latter I cannot found any reference on the website).
Edit:
I recently came across pysuggest which is 

a Top-N recommendation engine that
  implements a variety of recommendation
  algorithms. Top-N recommender systems,
  a personalized information filtering
  technology, are used to identify a set
  of N items that will be of interest to
  a certain user. In recent years, top-N
  recommender systems have been used in
  a number of different applications
  such to recommend products a customer
  will most likely buy; recommend
  movies, TV programs, or music a user
  will find enjoyable; identify
  web-pages that will be of interest; or
  even suggest alternate ways of
  searching for information.

