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The Apriori technique is used to extract common itemsets from a transactional database and create association rules. The terms "confidence" and "support" are employed. The frequency of occurrence of items is referred to as support, while confidence is a conditional probability. An item set is made up of the items in a transaction.

The Apriori method was the first algorithm for frequent itemset mining to be proposed. To decrease the search space, this method employs two steps: "join" and "prune." It is an iterative method for identifying the most common itemsets.

Determines the importance of certain itemsets. The support function aids in the identification of various levels of importance in itemsets. With the aid of unneeded itemet reduction, storage space is lowered. The algorithm's accuracy and efficiency were improvised.

The following are some of the advantages of apriori: Among the association rule learning algorithms, this is the simplest and most straightforward. The resulting rules are simple to understand and express to end users.

Some related enhancements are made based on the inherent flaws of the Apriori algorithm:

  • Using a new database mapping method to prevent continually scanning the database
  • prune frequent itemsets and candidate items even more in order to increase joining efficiency
  • Counting support using the overlap technique

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