Association Rules in Unsupervised Machine Learning

Association Rule Mining: A Complete Guide with Examples & MCQs

Association Rule Mining is one of the most powerful techniques in data mining and business analytics. It helps organizations discover hidden patterns, relationships, and correlations in large datasets — especially in transactional data.

If you've ever seen:

• "Customers who bought X also bought Y"

• Product bundling suggestions on Amazon

• Supermarket combo offers

You've witnessed Association Rule Mining in action.

1. What is Association Rule Mining?

Association Rule Mining is a data mining technique used to discover interesting relationships (associations) between variables in large datasets.

It answers questions like:

• Which products are frequently purchased together?

• What services are commonly used together?

• What patterns exist in customer behavior?

It is widely used in:

• Retail analytics

• E-commerce recommendation systems

• Healthcare data analysis

• Fraud detection

• Cybersecurity event correlation

2. Key Terminology in Association Rules

2.1 Itemset

A collection of one or more items.

Example:

• {Milk, Bread}

• {Laptop, Mouse}

2.2 Support

Support measures how frequently an itemset appears in the dataset.

Support(A)=Number of transactions containing A/Total number of transactions

Example:
If Milk appears in 4 out of 10 transactions:
Support(Milk) = 4/10 = 0.4

2.3 Confidence

Confidence measures how often items in Y appear in transactions that contain X.

Confidence(X→Y)=Support(X∪Y)/Support(X)

It represents the conditional probability:
P(Y | X)

2.4 Lift

Lift measures how much more likely Y is purchased when X is purchased.

Lift(X→Y)=Confidence(X→Y)/Support(Y)

• Lift > 1 → Positive association

• Lift = 1 → Independent

• Lift < 1 → Negative association

Let's calculate:

• Support(Milk) = 3/5 = 0.6

• Support(Bread) = 4/5 = 0.8

• Support(Milk, Bread) = 2/5 = 0.4

Confidence(Milk → Bread) = 0.4 / 0.6 = 0.67
Lift = 0.67 / 0.8 = 0.84

Since Lift < 1, Milk and Bread are weakly negatively associated in this dataset.

4. Apriori Algorithm

The most famous algorithm for association rule mining is:

Apriori Algorithm

Principle:

If an itemset is frequent, all its subsets must also be frequent.

Steps:

1. Generate frequent 1-itemsets

2. Generate candidate 2-itemsets

3. Prune using minimum support

4. Repeat until no more frequent itemsets

5. Generate rules using confidence threshold

5. Applications of Association Rule Mining

1. Market Basket Analysis

Identify products frequently purchased together.

2. Recommendation Systems

Amazon-style product recommendations.

3. Healthcare

Discover disease-drug relationships.

4. Cybersecurity

Detect unusual event combinations (useful in SIEM analysis).

5. Fraud Detection

Find suspicious transaction patterns.

6. Advantages

• Easy to interpret

• Unsupervised learning method

• Highly useful in retail analytics

• Scalable with algorithms like FP-Growth

7. Limitations

• Can generate too many rules

• High computational cost

• Requires careful threshold tuning

• May produce misleading rules without lift