Machine Learning Association Rule Mining Algorithms  Concepts of Association Rule Mining Association Rule Mining is a technique of data mining used for finding co-occurrence relationships and patterns in large datasets. It is employed to glean intriguing connections between variables in sizable databases. The relationships discovered in Association Rule Mining are represented in the form of rules, where the antecedent and consequent are a set of items.   There are several algorithms used in Association Rule Mining, such as Apriori, FP-Growth, ECLAT, and more. Among these algorithms, Apriori is the most widely used algorithm for Association Rule Mining.   The item sets that do not match the minimal support criterion are pruned by the Apriori algorithm after it generates a candidate set of item sets. The support threshold is a user-defined value that determines the minimum frequency of an item set to be considered as frequent. Association Rule Mining  Algorithm Define the problem and