Explain Apriori Algorithm.
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Ans.
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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