Apriori is an algorithm for frequent item set mining and association rule learning over relational databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database.
APRIORI algorithm was originally proposed by Agrawal in "Fast Algorithms for Mining Association Rules" in 1994 to find frequent itemsets and association rules in a transaction database. Here you can download a fast, trie-based, command-line implementation
S. Kontamwar, P. Warbhe, and P. Dubey. International Journal on Recent and Innovation Trends in Computing and Communication, 3 (4):
2471--2473(April 2015)
P. Kalaivani, D. Hanirex, and D. Kaliyamurthie. International Journal on Recent and Innovation Trends in Computing and Communication, 3 (3):
1142--1144(March 2015)
R. Agrawal, and R. Srikant. Proceedings of the 20th international conference on Very Large Data Bases (VLDB'94), page 478--499. Morgan Kaufmann, (September 1994)