Introduction - If you have any usage issues, please Google them yourself
In computer science and data mining, Apriori is a classic algorithm for learning association rules. Apriori is designed to operate on databases containing transactions. The algorithm attempts to find subsets which are common to at least a minimum number C (the cutoff, or confidence threshold) of the item sets. Apriori uses a "bottom up" approach, where frequent subsets are extended one item at a time (a step known as candidate generation) and groups of candidates are tested against the data. The algorithm terminates when no further successful extensions are found. Apriori uses breadth-first search and a hash tree structure to count candidate item sets efficiently.