Association rule mining is a technique used to discover relationships between variables in large datasets. It identifies patterns and correlations among items. The key concepts are itemsets, support, and confidence. The Apriori algorithm and FP-Growth approach are two common algorithms used. Apriori generates candidate itemsets in multiple passes over the data, while FP-Growth avoids candidate generation by building a tree structure. The Eclat algorithm also finds frequent itemsets but uses a vertical database format and depth-first search. These algorithms are applied to market basket analysis to understand customer purchasing behaviors.

1. Association Rule Mining

2. Association Rule Mining
 Proposed: by Agrawal et al in 1993.
 Definition: A technique in data mining for discovering interesting relationships between
variables in large datasets.
 Purpose: Identifies patterns, correlations, or causal structures among sets of items.
 Application:
 Market Basket Analysis
 Cross selling strategies
 Customer segmentation

3. Market Basket Analysis
 Object: To understand the purchase behavior of customers by identifying
the set of products bought together.
 Association rules:
A ⇒ D (customers who purchase product A also tend to buy product D at the same time)

4.  Key Concepts:
 Itemset – set of items (Eg : x = {milk, bread, cereal} is an itemset)
 K-itemset – an itemset with k items (Eg : x = {milk, bread, cereal} is a
 3-itemset)
 Support(s)-Fraction of transactions that contain both X & Y
 Confidence(c)-Measures how often items in Y appear in transactions that contain
X
 Process:
 Step 1: Collect and preprocess data (sales transactions).
 Step 2: Identify frequent itemsets using algorithms (e.g., Apriori, FP-Growth, Eclat
Algorithm).
 Step 3: Generate association rules from these itemsets.
 Step 4: Evaluate the rules based on metrics (support, confidence, lift).

5. Market Basket Analysis - Example
 Ex-{Shoes,Trouser} =>Shirt
 S=Frequent(Shoes,Trouser, Shirt)/N
=2/4
=0.5
 C=Frequent(Shoes,Trouser, Shirt)/Frequent(Shoes,Trouser)
=2/3
=0.67

6. Apriori Algorithm
 The Apriori algorithm is a technique for finding frequent itemsets in transactional data.
 Steps :
 Define Minimum Support and Minimum Confidence Thresholds
 Find Frequent Individual Items
 Generate Candidate Itemsets of Size 2
 Prune Infrequent Candidate Itemsets
 Generate Candidate Itemsets of Size 3
 Continue Generating and Pruning Candidate Itemsets Until No More Frequent Itemsets Are
Found
 Generate Association Rules from Frequent Itemsets

7. Apriori Algorithm - Example

8. Suppose Min_confidence = 40%
If confidence >= min_condidence
We accept the Rule
{C1R,A2R,A2C}
Subset={C1R,A2R},{C1R,A2C},{A2R,A2C},{C1R},{A2R},{A2C}
 Rule 1- {C1R,A2R}
Confidence=support(C1R,A2R,A2C)/support(C1R,A2R)
=(2/4)/(2/4)
=100%>40%
So Rule 1 is Accepted.
 Rule 2- {C1R,A2C}
Confidence=support(C1R,A2R,A2C)/support(C1R,A2C)
=(2/4)/(3/4)
=2/3
=66.6%>40%
So Rule 2 is Accepted.
Like this we check all subsets .

9. Apriori Advantages/Disadvantages
 Advantages
 Uses large itemset property
 Easily parallized
 Easy to implement
 Disadvantages
 Assumes transaction database is more resident
 Requiers many database scans

10. Frequent Pattern Growth Approach
 An efficient data mining algorithm for finding frequent itemsets without
candidate generation.
 Ideal for large datasets where the Apriori algorithm's performance degrades due
to the generation of a large number of candidate sets.
 Steps
 Calculate minimum support.
 Find frequency of occurence.
 Prioritize the items
 Order the items according to prioriy
 Validation

11. Frequent Pattern Growth - Example
TID Item
1 E,A,D,B
2 D,A,C,E,B
3 C,A,B,E
4 B,A,D
5 D
6 D,B
7 A,D,E
8 B,C
Assume minimum support = 30%
Minimum support count = (30/100 * 8 ) = 2.4

12. TID Item Ordered item
1 E,A,D,B B,A,D,E
2 D,A,C,E,B B,D,A,E,C
3 C,A,B,E B,A,E,C
4 B,A,D B,D,A
5 D D
6 D,B B,D
7 A,D,E D,A,E
8 B,C B,C
TID Frequency
A 5
B 6
C 3
D 6
E 4
TID Frequency Priority
A 5 3
B 6 1
C 3 5
D 6 2
E 4 4

14. Advantages of FP-Growth
 Increased Efficiency
 Requires only two passes over the dataset, minimizing the time and resources
needed for data processing.
 Significantly faster as it avoids the exhaustive candidate generation phase that
Apriori undergoes.
 Elimination of Candidate Generation
 Directly mines the frequent itemsets from the FP-tree without generating
candidate itemsets.
 Lower Memory Requirements
 The FP-tree data structure is compact and often requires less memory than
storing extensive candidate sets, especially beneficial for dense datasets.

15. Eclat Algorithm
 Eclat stands for Equivalence Class Clustering and bottom-up
Lattice Traversal.
 It is a depth-first search algorithm used for mining frequent
itemsets, introduced as an efficient alternative to the Apriori
algorithm.
 Focuses on vertical database format, where transactions are
represented as sets of items.

16. Example for Eclat Algorithm
T ID Itemset
T1 Bread,Butter,Jam
T2 Butter, Coke
T3 Butter, Milk
T4 Bread, Butter, Coke
T5 Bread, Milk,
T6 Butter, Milk,
T7 Bread, Milk
T8 Bread, Butter, Milk, Jam
T9 Bread, Butter,Milk
T ID Bread Butter Milk Coke Jam
T1 1 1 0 0 1
T2 0 1 0 1 0
T3 0 1 1 0 0
T4 1 1 0 1 0
T5 1 0 1 0 0
T6 0 1 1 0 0
T7 1 0 1 0 0
T8 1 1 1 0 1
T9 1 1 1 0 0
K=1,Minimum Support=2
Item T ID Set
Bread {T1,T4,T5,T7,T8,T9}
Butter {T1,T2,T3,T4,T6,T8,T9}
Milk {T3,T5,T6,T7,T8,T9}
Coke {T2,T4}
Jam {T1,T8}

17. Item T ID Set
{Bread,Butter} {T1,T4,T8,T9}
{Bread,Milk} {T5,T7,T8,T9}
{Bread,Coke} {T4}
{Bread,Jam} {T1,T8}
{Butter,Milk} {T3,T6,T8,T9}
{Butter,Coke} {T2,T4}
{Butter,Jam} {T1,T8}
{Milk,Jam} {T8}
Item T ID Set
{Bread,Butter,Milk} {{T8,T9}
{Bread,Butter,Jam} {T1,T8}
{Bread,Butter,Coke} {T4}
{Bread,Milk,Jam} {T8}
Item T ID Set
{Bread,Butter,Milk,Jam} {T8}
K=2 K=3
K=4

18. Item Bought Recommended Products
Bread Butter
Bread Milk
Bread Jam
Butter Milk
Butter Jam
Bread and Butter Milk
Bread and Butter Jam
• stop at k=4 because there are no
more item-Tid Set pairs to combine
• minimum support =2, we conclude
the following rules form the given
dataset

19. Eclat Algorithm Advantages/Disadvantages
 Advantages
 Depth-first search reduces memory requirements
 Usually (considerably) faster than Apriori
 No need to scan the database to find the support of (k+1)
itemsets, for k>=1
 Disadvantages
 The TID-sets can be quite long, hence expensive to manipulate

20. Thank You!