The Apriori algorithm is a data mining algorithm used for frequent item set mining and association rule learning over relational databases. It was proposed by R. Agrawal and R. Srikant in 1994. The algorithm proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as they appear sufficiently often in the database. The key concept of the Apriori algorithm is its anti-monotonicity of support measure, which assumes that all subsets of a frequent itemset must be frequent. The algorithm uses an iterative approach or level-wise search where k-frequent itemsets are used to find k+1 itemsets. To improve the efficiency of level-wise generation of frequent itemsets, an important property is used called the Apriori property, which helps by reducing the search space.
The Apriori algorithm is generally used on databases containing transactions, such as collections of items bought by customers or details of website frequentation or IP addresses. The algorithm is also called frequent pattern mining and is used to create association rules between different objects. The primary objective of the Apriori algorithm is to find the most frequent itemset in the given database. The algorithm uses two steps, "join" and "prune," to reduce the search space.
The Apriori algorithm has some limitations, such as being slow and not being an efficient approach for large datasets. However, it is still considered the foundational algorithm in basket analysis, which is the study of a clients basket while shopping. The goal of basket analysis is to find combinations of products that are often bought together, which are called frequent itemsets. The Apriori algorithm can be used in other fields, such as education, medical, forestry, and autocomplete tool.
In summary, the Apriori algorithm is a data mining algorithm used for frequent item set mining and association rule learning over relational databases. It uses an iterative approach to identify the most frequent individual items in the database and extend them to larger and larger item sets. The algorithm is generally used on databases containing transactions and is also called frequent pattern mining. Although it has some limitations, it is still considered the foundational algorithm in basket analysis and can be used in other fields as well.
