The Apriori Algorithm is a popular method used in data mining to discover frequent itemsets in large datasets. It works by identifying items that frequently occur together in transactions, which helps in understanding patterns and relationships within the data. For example, in a grocery store, it can reveal that customers who buy bread often also buy butter. This algorithm operates on the principle of "apriori," meaning it uses prior knowledge of frequent itemsets to reduce the search space. By generating candidate itemsets and pruning those that do not meet a minimum support threshold, the Apriori Algorithm efficiently finds valuable insights for applications like market basket analysis and recommendation systems.