3. Definition Of Apriori Algorithm:
Apriori: is a seminal algorithm for mining frequent itemsets for Boolean association
rules.
Apriori uses a “Bottom Up Approach”, where frequent subsets are extended one item
at a time (a step known as candidate generation), and groups of candidates are tested
against the data.
4. Key Concepts:
A frequent itemset: is a set of items that occur together frequently in a dataset.
A frequent itemsets: All the sets which contain the item with the minimum support
(denoted by Li for ith itemset).
Apriori property: All nonempty subsets of a frequent itemset must also be frequent.
11. Apriori Algorithm: An Example Cont.
The algorithm uses L3 join L3 to generate a candidate set of 4-itemsets, C4.
the join results in {I1, I2, I3, I5}
itemset {I1, I2, I3, I5} is pruned because its subset {I2, I3, I5} is not frequent.
C4= Ø , and the algorithm terminates.
14. Generating Association Rules from Frequent Itemsets
Once the frequent itemsets from transactions in a database D have been found, it is
straightforward to generate strong association rules from them.
strong association rules satisfy both minimum support and minimum confidence.
For each frequent itemset l, generate all nonempty subsets of l.
output the rule if confidence min conf threshold.
Because the rules are generated from frequent itemsets, each one automatically satisfies
the minimum support.
22. Generating Association Rules from Frequent Itemsets
Part(b): Generate the strong Association Rules from the Frequent Itemsets
the minimum confidence threshold = 70%
The frequent itemset X = {Milk, Bread, Eggs} = {1,2,3}
The nonempty subsets of X are {1, 2}, {1, 3}, {2, 3}, {1}, {2}, and {3}.
