The Apriori Algorithm is a fundamental machine learning technique used for mining frequent itemsets and generating association rules from transaction datasets. It is particularly effective in market basket analysis, where the goal is to identify relationships between products that are frequently purchased together. For instance, if customers often buy bread and butter together, the algorithm can help uncover this association, which can be valuable for marketing strategies and inventory management ¹².

The algorithm operates on the principle that any subset of a frequent itemset must also be frequent. This means that if a combination of items is found to be popular, all of its subsets are also likely to be popular. The Apriori Algorithm requires prior knowledge of the dataset, specifically the minimum support threshold, which determines how often an itemset must appear in the dataset to be considered frequent ³⁵.

Despite its usefulness, the Apriori Algorithm has limitations, such as inefficiency when handling large datasets due to the need to scan the entire database multiple times ⁵.