A collection of zero or more items is known as an itemset in association analysis. The term "k-itemset" refers to an itemset with k items. A three-itemset, for example, may be Beer, Diapers, and Milk. An itemset with no items is called a null (or empty) set.
The Apriori Algorithm is commonly used for frequent pattern mining, and FP-growth is an upgraded version of it (AKA Association Rule Mining). It's an analytical technique for identifying common patterns or correlations in data sets.
From a transactional database, the Apriori algorithm is used to mine frequent itemsets and create association rules. "Support" and "confidence" are the parameters that are employed. The frequency of occurrence of items is referred to as support, and the conditional probability is referred to as confidence. An item set is made up of items in a transaction.