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apriori-gen function

The apriori-gen function is a candidate generation procedure used in the Apriori algorithm for association rule mining that constructs candidate frequent itemsets of a given size from verified frequent itemsets of the preceding smaller size. It operates through two sequential phases consisting of a join step and a prune step. In the join step, frequent itemsets containing k minus one items are combined with each other to produce potential candidate itemsets of size k, typically by merging pairs that share their first k minus two items in common. In the prune step, the function applies the downward-closure property of support, which dictates that all subsets of a frequent itemset must also be frequent, and immediately discards any candidate itemset containing at least one sub-itemset that is not in the prior frequent set. This systematic elimination minimizes the number of candidate itemsets that must be evaluated against the underlying dataset, significantly improving computational efficiency during frequent pattern discovery.

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Fast Algorithms for Mining Association Rules in Large Databases

Fast Algorithms for Mining Association Rules in Large Databases

Rakesh Agrawal, Ramakrishnan Srikant

OrganizationsIBMUniversity of Wisconsin Madison

Why you should read this

Presents the Apriori and AprioriHybrid algorithms, establishing an efficient, linearly scalable foundation for mining frequent itemsets and association rules across massive transaction databases.

We consider the problem of discovering association rules between items in a large database of sales transactions. We present two new algorithms for solving this problem that are fundamentally different from the known algorithms. Empirical evaluation shows that these algorithms outperform the known algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems. We also show how the best features of the two proposed algorithms can be combined into a hybrid algorithm, called AprioriHybrid. Scale-up experiments show that AprioriHybrid scales linearly with the number of transactions. AprioriHybrid also has excellent scale-up properties with respect to the transaction size and the number of items in the database.

Added

2026-09-06