The document discusses the Apriori algorithm for mining frequent itemsets. It defines key concepts like frequent itemsets and the Apriori property. The steps of the Apriori algorithm are described through an example where transactions of items are analyzed to find frequent itemsets. Frequent itemsets that satisfy a minimum support threshold are identified. Association rules are then generated from these frequent itemsets if they meet a minimum confidence threshold. Another example applies the full Apriori algorithm to a dataset to identify frequent itemsets and generate strong association rules.

1. LAB1:
APRIORI ALGORITHM
Prepared by: TA. Amany Adel Ali
a.adel@fci-cu.edu.eg

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.

5. Apriori Algorithm Example
 A typical example of frequent itemset mining is market basket analysis

6. Steps To Perform Apriori Algorithm

7. Apriori Algorithm: An Example
 |D| = 9
 min_sup = 2

8. Apriori Algorithm: An Example Cont.

9. Apriori Algorithm: An Example Cont.

10. Apriori Algorithm: An Example Cont.

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.

12. Generating Association Rules from Frequent Itemsets

13. Generating Association Rules from Frequent Itemsets

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.

15. Generating Association Rules from Frequent Itemsets

16. Generating Association Rules from Frequent Itemsets
 the minimum confidence threshold = 70%
 The frequent itemset X = {I1, I2,I5}.
 The nonempty subsets of X are {I1, I2}, {I1, I5}, {I2, I5}, {I1}, {I2}, and {I5}.
{I1, I2} → I5, confidence = 2/4 = 50%
{I1, I5} → I2, confidence = 2/2 = 100% Strong
{I2, I5} → I1, confidence = 2/2 = 100% Strong
{I1} → {I2, I5}, confidence = 2/6 = 33%
{I2} → {I1, I5}, confidence = 2/7 = 29%
{I5} → {I1, I2} confidence = 2/2 = 100% Strong

17. Generating Association Rules from Frequent Itemsets
 the minimum confidence threshold = 70%
 The frequent itemset Y = {I1, I2,I3}.
 The nonempty subsets of Y are {I1, I2}, {I1, I3}, {I2, I3}, {I1}, {I2}, and {I3}.
{I1, I2} → I3, confidence = 2/4 = 50%
{I1, I3} → I2, confidence = 2/4 = 50%
{I2, I3} → I1, confidence = 2/4 = 50%
{I1} → {I2, I3}, confidence = 2/6 = 33%
{I2} → {I1, I3}, confidence = 2/7 = 29%
{I3} → {I1, I2} confidence = 2/6 = 33%

18. Apriori Algorithm: Another Example
 Part(a): Apply the Apriori algorithm to the following data set:

19. Step-1: Index The Data

20. Step-2: Calculate The Support For Each One

21. Step-3: Continue To Calculate The 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}.

23. Generating Association Rules from Frequent Itemsets

24. Shopping Basket Analysis using SQL Server and Visual Server
 https://www.youtube.com/watch?v=GyakcKKGwGA