1. Transactional data Algorithm Applications
Association Rules
Transactional data
Algorithm
Applications
11/26/2018
CSE591: Data Mining by H. Liu

2. Market Basket Analysis
Transactional data
Sparse matrix: thousands of columns, each row has only dozens of values
Items
Itemsets: transactions (TID)
A most cited example “diapers and beer”
11/26/2018
CSE591: Data Mining by H. Liu

3. Association rule mining
Finding interesting association or correlation relationships
Defining interesting association rules
Support (P(AB))
Confidence (P(B|A))
An association rule
A -> B
11/26/2018
CSE591: Data Mining by H. Liu

4. Finding association rules
Finding frequent itemsets
downward closure property (or anti-monotonic)
Finding association rules from frequent itemsets
Frequent Itemsets
minisup
from 1-itemset to k-itemset
Association rules
miniconf
satisfying minimum confidence
11/26/2018
CSE591: Data Mining by H. Liu

5. CSE591: Data Mining by H. Liu
Apriori
Level-wise search
Anti-monotone property
The procedure
Join
prune
An example
11/26/2018
CSE591: Data Mining by H. Liu

6. CSE591: Data Mining by H. Liu
Issues
Efficiency
Number of association rules
size of data vs. size of association rules
Post-processing
Applications
combining association rules with classification
emergency patterns
11/26/2018
CSE591: Data Mining by H. Liu

7. Types of association rules
Single dimensional association rules
Multiple dimensional association rules
Multi-level association rules
11/26/2018
CSE591: Data Mining by H. Liu