Association Rule Learning is a data mining technique used to discover interesting relationships and patterns among items in large datasets. It is particularly effective in identifying correlations between items that frequently occur together. This method is widely applied in various domains, such as market basket analysis, where it helps retailers understand customer purchasing behavior by revealing which products are often bought together ¹⁴.

The process of Association Rule Learning involves generating rules that express these relationships. The rules are typically evaluated based on three key metrics: support, confidence, and lift. Support measures how often an itemset appears in the dataset, confidence indicates the likelihood that a rule is true, and lift assesses the strength of the association between items compared to their independent occurrence ³⁴.

One of the most popular algorithms used in Association Rule Learning is the Apriori algorithm. This algorithm identifies frequent itemsets and generates rules based on specified thresholds for support and confidence. It operates by iteratively finding itemsets that meet the minimum support requirement and then deriving rules from these itemsets ⁵.