The TD_Apriori function discovers the patterns (group of items occurred together) and calculates several statistical measures to understand how likely one set of items influence another. A typical input table for the Apriori function is a set of sales transactions, with a column of purchased items and a column of something by which to group the purchased items.
Apriori is an algorithm used 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. The frequent item sets determined by Apriori can be used to determine association rules which highlight general trends in the database: this has applications in domains such as market basket analysis.