1. Data Mining Techniques Association Rule

2. What Is Association Mining?
Association Rule Mining
Finding frequent patterns, associations, correlations, or causal structures among item sets in transaction databases, relational databases, and other information repositories
Applications
Market basket analysis (marketing strategy: items to put on sale at reduced prices), cross-marketing, catalog design, shelf space layout design, etc
Examples
Rule form: Body ® Head [Support, Confidence].
buys(x, “Computer”) ® buys(x, “Software”) [2%, 60%]
major(x, “CS”) ^ takes(x, “DB”) ® grade(x, “A”) [1%, 75%]

3. Market Basket Analysis
Typically, association rules are considered interesting if they satisfy both
a minimum support threshold and a minimum confidence threshold.

4. Rule Measures: Support and Confidence
Let minimum support 50%, and minimum confidence 50%, we have
A  C [50%, 66.6%]
C  A [50%, 100%]

5. Support & Confidence

6. Association Rule: Basic Concepts
Given
(1) database of transactions,
(2) each transaction is a list of items (purchased by a customer in a visit)
Find all rules that correlate the presence of one set of items with that of another set of items
Find all the rules A  B with minimum confidence and support
support, s, P(A  B)
confidence, c, P(B|A)

7. Terminologies Item Itemset 1-Itemset 2-Itemset I1, I2, I3, …
A, B, C, …
Itemset
{I1}, {I1, I7}, {I2, I3, I5}, …
{A}, {A, G}, {B, C, E}, …
1-Itemset
{I1}, {I2}, {A}, …
2-Itemset
{I1, I7}, {I3, I5}, {A, G}, …

8. Terminologies K-Itemset Frequent (Large) K-Itemset Association Rule
If the length of the itemset is K
Frequent (Large) K-Itemset
If the length of the itemset is K and the itemset satisfies a minimum support threshold.
Association Rule
If a rule satisfies both a minimum support threshold and a minimum confidence threshold

9. Analysis
The number of itemsets of a given cardinality tends to grow exponentially

10. Fast Algorithms for Mining Association Rules

11. Mining Association Rules: Apriori Principle
Min. support 50%
Min. confidence 50%
For rule A  C:
support = support({A  C}) = 50%
confidence = support({A  C})/support({A}) = 66.6%
The Apriori principle:
Any subset of a frequent itemset must be frequent

12. Mining Frequent Itemsets: the Key Step
Find the frequent itemsets: the sets of items that have minimum support
A subset of a frequent itemset must also be a frequent itemset
i.e., if {AB} is a frequent itemset, both {A} and {B} should be a frequent itemset
Iteratively find frequent itemsets with cardinality from 1 to k (k-itemset)
Use the frequent itemsets to generate association rules

13. Example Database D 1 3 4 2 3 5 1 2 3 5 2 5 scan D count C1 C1 count
2 5
scan D
count C1
C1 count
generate L1 L1 1 2 3 5
scan D
count C2
C2 count
generate L2 L2 13 23 25 35 C2 12 15
generate C2
scan D
count C3
C3 count
generate L3 L3
235 C3
generate C3

14. Example of Generating Candidates
L3={abc, abd, acd, ace, bcd}
Self-joining: L3*L3
abcd from abc and abd
acde from acd and ace
Pruning:
acde is removed because ade is not in L3
C4={abcd}

15. Example

16. Apriori Algorithm

17. Apriori Algorithm

18. Apriori Algorithm

19. Exercise 4
min-sup = 20%
min-conf = 80%

20. Demo-IBM Intelligent Minner

21. Demo Database

25. Multi-Dimensional Association
Single-Dimensional (Intra-Dimension) Rules: Single Dimension (Predicate) with Multiple Occurrences.
buys(X, “milk”)  buys(X, “bread”)
Multi-Dimensional Rules:  2 Dimensions
Inter-dimension association rules (no repeated predicates)
age(X,”19-25”)  occupation(X,“student”)  buys(X,“coke”)
hybrid-dimension association rules (repeated predicates)
age(X,”19-25”)  buys(X, “popcorn”)  buys(X, “coke”)
Categorical (Nominal) Attributes
finite number of possible values, no ordering among values
Quantitative Attributes
numeric, implicit ordering among values

26. Exercise 5
min-sup = 20%
min-conf = 80%

27. Research Topics Quantitative Association Rules
buys (bread, 5) ® buys (milk, 3)
Weighted Association Rules
High Utility Association Rules
Non-redundant Association Rule
Constrained Association Rules Mining
Multi-dimensional Association Rules
Generalized Association Rules
Negative Association Rules
Incremental Mining Association Rules
Data Stream Association Rule Mining
Interactive Mining Association Rules