Explain Apriori Algorithm.


1.     Apriori is a seminal algorithm for mining frequent itemsets for Boolean association rules.
2.     The name of the algorithm is based on the fact that the algorithm uses prior knowledge of frequent itemset properties.
3.     Apriori employs an iterative approach known as a level-wise search, where k-itemsets are used to explore (k+1)-itemsets.
4.     Apriori property is used to improve the efficiency of the level-wise generation of frequent itemsets.
5.     Apriori property states that all nonempty subsets of a frequent itemset must also be frequent.
6.     The property used in the algorithm uses two-step process. Using Lk-1,  Lk is found for k >= 2.
·       The join step: To find Lk, a set of candidate k-itemsets is generated by joining Lk-1 with itself. This set of candidates is denoted Ck. In the join component, Lk-1 is joined with Lk-1 to generate potential candidates.
·       The prune step: Ck is a superset of Lk, that is, its members may or may not be frequent, but all of the frequent k-itemsets are included in Ck.  The prune component employs the Apriori property to remove candidates that have a subset that is not frequent.


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