Frequent itemsets refer to sets of items that frequently appear together in transactions within a dataset. In other words, these are combinations of items that occur with a frequency above a specified threshold.

Association rules are logical relationships that describe the associations between different sets of items in a dataset. These rules are typically expressed in the form of if-then statements, indicating that the presence of certain items (the antecedent) implies the presence of other items (the consequent) with a certain level of confidence.

In essence, frequent itemsets highlight item combinations that occur frequently, while association rules uncover patterns and relationships between items within these frequent itemsets.