Implementing Apriori Algorithm in c++/java/python

Part 1:
create 30 items usually seen in amazon, k-mart, or any other supermarkets (e.g. diapers, clothes, etc.). (1) create a database of 20 transactions each containing some of these items. the information can be stored in a file, or a dbms (e.g. oracle). (2) repeat (1) by creating 4 additional, different databases each containing 20 transactions. using apriori, generate and print out all the association rules and the input transactions for each of the 5 transaction databases you created (support and confidence should be user-determined parameter values, so the output should show different support and confidence values).

Part 2:
Implement the brute force method and compare the brute force method
with the Apriori algorithm on each of the 5 transaction databases you
created. Present computation (CPU or clock) time to demonstrate that
the Apriori algorithm is faster than the brute force method on each of the
5 transaction databases. The brute force method and Apriori algorithm
should output the same association rules on each database.
 The brute force method for finding frequent itemsets works as follows.
Enumerate and generate all possible 1-itemsets and 2-itemsets. There are
30 items, so there are 435 possible 2-itemsets totally. Check to see
whether each possible 1-itemset/2-itemset is frequent. Then enumerate
and generate all possible 3-itemsets. There are 4060 possible 3-itemsets
totally. Check to see whether each possible 3-itemset is frequent. Keep
on doing so until you see none of the possible
k-itemsets is frequent for
some
k, at which point the brute force method terminates without
generating (k+1)-itemsets.