Data Warehousing and Data Mining · Unit 6
Association Rule Mining
Exam-focused notes for Association Rule Mining (Data Warehousing and Data Mining, BIT454): what the TU syllabus asks and how it has actually been tested, with 2 solved past questions from this unit.
What this unit covers
- Market basket analysis concept
- Frequent itemset generation
- Apriori algorithm
- Strong association rules
- Support and confidence measures
- Laplace smoothing
Apriori algorithm
Generate the frequent itemset from the following data using the Apriori algorithm and find the strong association rules. Minimum Support = 60%, Minimum Confidence = 75%.
| TID | Items |
|---|---|
| 1 | {A, C, D} |
| 2 | {B, C, D} |
| 3 | {A, B, C, D} |
| 4 | {B, D} |
| 5 | {A, B, C, D} |
[10]
- Transactions: - T1: {A, C, D} - T2: {B, C, D} - T3: {A, B, C, D} - T4: {B, D} - T5: {A, B, C, D} - Total transactions $N = 5$ - Minimum Support = 60% → absolute count = $0.60 \times 5 = 3$ - Minimum Confidence = 75% --- Item Count Support Frequent? ------...
Full solved answer →Market basket analysis concept
What is the concept behind market basket analysis?How do you generate frequent item sets using Apriori algorithm? Explain.[4+6]
(a) Concept Behind Market Basket Analysis Market Basket Analysis is a data mining technique used to discover associations and relationships between items purchased together by customers. 1. Purpose: - Identifies which products are frequently bought together...
Full solved answer →Make Unit 6 stick
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