This program is a very efficient implementation of APRIORI algorithm proposed by Rakesh Agrawal and Ramakrishnan Srikant. APRIORI is the most basic and well-known algorithm to find frequent itemsets in a transactional database.
A transactional database consists of sequence of transaction: . A transaction is a set of items (). Transactions are often called baskets, referring to the primary application domain (i.e. market-basket analysis). A set of items is often called itemset by the data mining community. The (absolute) support or the occurrence of (denoted by ) is the number of transactions that are supersets of (i.e. that contain ). The realtive support is the absolute support divided by the number of transactions (i.e. n). An itemset is frequent if its support is greater or equal than a threshold value.
In the frequent itemset mining problem a transaction database and a relative support threshold (traditionally denoted by min_supp) is given and we have to find all frequent itemsets.