AprioriAll
AprioriAll is an extension of the Apriori algorithm, a popular data mining technique used for frequent itemset mining. While the original Apriori algorithm focuses on finding all itemsets that meet a minimum support threshold, AprioriAll aims to discover all itemsets, regardless of their support, up to a specified maximum length. This means it can find very short, frequent patterns that might otherwise be missed if a minimum support is set too high.
The core idea behind AprioriAll is to leverage the downward-closed property of frequent itemsets, which states
The algorithm typically involves two main steps: candidate generation and candidate pruning. Candidate generation produces a