Association rule mining (ARM) is a data mining technique used to discover relationships between variables in large databases. It finds frequent patterns, associations, correlations, or causal structures among sets of items or objects in transactional databases. ARM is commonly used in retail by analyzing customer purchase histories to find relationships between products customers buy together. The Apriori algorithm is commonly used for ARM. It generates candidate item sets and then scans the database to determine which item sets meet the minimum support and confidence thresholds.

2. Association Rules Mining(ARM)
• Association rule mining is the data mining process of finding
the rules that may govern associations and causal objects
between sets of items.
• ARM is also called Market Basket Analysis(MBA) & affinity
analysis.
• Set of Items in a transaction is called is called Market Basket.
• Mostly used in RETAIL.
• If ‘A’then ‘B’ {A=>B} [Where A is antecedent & B is consequent ]

3. Example

4. Two Major terms in ARM
• Support: (S)- Percentage of Transaction (T) that
contains both A and B.
{A=>B} = P(AꓴB) it measures frequency of association.
• Confidence: (C)- In a Transaction Set (T) if C is the %
times of times B is in all the transaction Containing A.
C=P(B/A)= P(AꓴB)/P(A) ConditionProbability

5. Parameters in ARM
• Finding all items that appears frequently in
transaction.(Minimum Support Count).
• Finding Strong Associations among frequent items

6. Association Rules Mining(ARM)

7. Apriori Algorithm
Algo: It is idea to generate Candidate item sets of a given size and then scan
dataset to check if their counts are really large.
• All item sets are candidate in the first pass,any item with less them
specified support value is eliminated.
• We create n number of item sets like one,two –n
• Generate association rules which have confidence values greater then or
equal to specified min confidence.

8. Apriori Algorithm
Question: For the Following transaction given data set ,generates rules using Apriori
algorithm .Consider the values as SUPPORT=22% and CONFIDENCE=70%
TransactionID Items Purchased
1 I1,I2,I5
2 I2,I4
3 I2,I3
4 I1,I2,I4
5 I1,I3
6 I2,I3
7 I1,I3
8 I1I2,I3,I5
9 I1,I2,I3
Item Frequency Support
I1 6 6/9=66%
I2 7 7/9=80%
I3 6 6/9=66%
I4 2 2/9=22.2%
I5 2 2/9=22.2%
C1
All item
support >=22%
Minimum Frequency Support

9. Apriori Algorithm
Now Generate pairs of itemsets C3
Item Set Frequency Support
I1,I2 4 4/9=44.4%
I1,I3 4 4/9=44.4%
I1,I4 1 1/9=11.1%
I1,I5 2 2/9=22.2%
I2,I3 4 4/9=44.4%
I2,I4 2 2/9=22.2%
I2,I5 2 2/9=22.2%
I3,I4 0 0
I3,I5 1 1/9=11.1%
I4,I5 0 0
ItemSet Frequency Support
I1,I2,I3 2 2/9=22.2%
I1,I2,I5 2 2/9=22.2%
Item Set Frequency
(I1,I2)I5 2/4=50%
(I1,I5)I2 2/2=100%
(I2,I5)I1 2/2=100%
I1(I2,I5) 2/6=33%
I2(I1,I5) 2/7=29%
I5(I2,I1) 2/2=100%

10. Thank You…