1. (Understanding customer behavior Using data mining skills)
Lecture 10 Knowledge Induction using association rule and decision tree
(Understanding customer behavior
Using data mining skills)

2. Part I. Association Rule Analysis
Market Basket Analysis

3. What Is Association Mining?
Association rule mining:
Finding frequent patterns, associations, correlations, or causal structures among sets of items or objects in transaction databases, relational databases, and other information repositories.
Applications:
Market basket analysis, cross-marketing, catalog design, loss-leader analysis, clustering, classification, etc.
Examples:
Rule form: “Body ® Head [support, confidence]”
buys(x, “diapers”) ® buys(x, “beers”) [0.5%, 60%]
major(x, “CS”) ^ takes(x, “DB”) ® grade(x, “A”) [1%, 75%]

4. Support and Confidence
Percent of samples contain both A and B
support(A  B) = P(A ∩ B)
Confidence
Percent of A samples also containing B
confidence(A  B) = P(B|A)
Example
computer  financial_management_software
[support = 2%, confidence = 60%]

5. Association Rules: 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
e.g., 98% of people who purchase tires and auto accessories also get automotive services done
Applications
Home Electronics - What other products should the store stocks up?
Retailing – Shelf design, promotion structuring, direct marketing

6. Rule Measures: Support and Confidence
Find all the rules A  C with minimum confidence and support
Support (s) probability that a transaction contains {A & C}
Confidence (c) conditional probability that a transaction having {A} also contains {C}
Customer
buys both
Customer
buys diaper
Customer
buys beer
Let minimum support 50%, and minimum confidence 50%, we have
A  C (50%, 66.6%)
C  A (50%, 100%)

7. Mining Association Rules: An Example
Target:
Min. support 50%
Min. confidence 50%
For rule A  C:
support = support({A, C}) = 50%
confidence = support({A, C})/support({A}) = 66.6%

8. An Example of Market Basket(1)
There are 8 transactions on three items on A, B , C.
Check associations for below two cases.
(1) A  B
(2) (A, B)  C #
Basket 1 A 2 B 3 C 4
A, B 5
A, C 6
B, C 7
A, B, C 8

9. An Example of Market Basket(1(2)
Basic probabilities are below:
(1) AB
(2) (A, B)  C
LHS
P(A) = 5/8 = 0.625
P(A,B) = 3/8 = 0.375
RHS
P(B) = 5/8 = 0.625
P(C) = 5/8 = 0.625
Coverage
LHS = 0.625
LHS = 0.375
Support
P(A∩B) = 3/8 = 0.375
P((A,B)∩C)) = 2/8 =0.25
Confidence
P(B|A)=0.375/0.625=0.6
P(C|(A,B))=0.25/0.375=0.7
Lift
0.375/(0.625*0.625)=0.96
0.25/(0.375*0.625)=1.07
Leverage =
= 0.016

10. Lift What are good association rules? (How to interpret them?)
If lift is close to 1, it means there is no association between two items (sets).
If lift is greater than 1, it means there is a positive association between two items (sets).
If lift is less than 1, it means there is a negative association between two items (sets).

11. Leverage Leverage = P(A∩B) - P(A)*P(B) , it has three types
① Two items (sets) are positively associated
② Two items (sets) are independent
③Two items (sets) are negarively associated

12. Lab on Association Rules(1)
SPSS Clementine, SAS Enterprise Miner have association rules softwares.
This exercise uses Magnum Opus.
Go to and download Magnum Opus evaluation version ( click)

13. After you install the problem, you can see below initial screen
After you install the problem, you can see below initial screen. From menu, choose File – Import Data (Ctrl – O).

14. Demo Data sets are already there
Demo Data sets are already there. Magnum Opus has two types of data sets available: (transaction data: *.idi, *.itl) and (attribute-value data: *.data, *.nam)
Data format has below two types:(*.idi, *.itl).
idi
(identifier-item file)
itl
(item list file)
001, apples 001, oranges 001, bananas 002, apples 002, carrots 002, lettuce 002, tomatoes
apples, oranges, bananas apples, carrots, lettuce, tomatoes

15. If you open tutorial.idi using note pad, you can see the file inside as left.
The example left has 5 transactions (baskets)

16. File – Import Data, or click . click Tutorial.idi
Check Identifier – item file and click Next >.

17. Click Yes and click Next > …

18. Click Next > …
What percentage of whole file you want to use? Type 50% and click Next > …

19. click Import Data를 클릭
Then, you can see a screen like below left.

20. Set things as they are. Click GO Search by: LIFT Minimum lift: 1
Maximum no. of rules: 10
Click GO

21. Results are saved in tutorial.out file. Below are rules derived:
lettuce & carrots
are associated with tomatoes
with strength = 0.857
coverage = 0.042: 21 cases satisfy the LHS
support = 0.036: 18 cases satisfy both the LHS and the RHS
lift 3.51: the strength is 3.51 times greater than the strength
if there were no association
leverage = : the support is (12.9 cases) greater than

22. lettuce & carrots  tomatoes
When Lettuce and carrots are purchase then they buy tomatoes
coverage = 0.042: 21 cases satisfy the LHS
LHS(lettuce & carrots) = 21/500 = 0.042
support = 0.036: 18 cases satisfy both the LHS and the RHS
P((lettuce & carrots) ∩ tomatoes)) = 18/500 = 0.036
strength(confidence) = 0.857
P(support|LHS)= 18/21 = 0.036/0.042 = 0.857

23. lift 3.51: the strength is 3.51 times greater than the strength if there were no association
즉, (18/21)/(122/500) = 3.51
leverage = : the support is (12.9 cases) greater than if there were no association
P(LHS ∩ RHS) – P(A)*P(B) = – 0.042*0.244 =

24. Part II: Knowledge Induction from Data

25. Poll: Which data mining technique..?

26. Classification Process Step 1: Model Construction
Algorithms
Training
Data
Classifier
(Model)
IF rank = ‘professor’
OR years > 6
THEN tenured = ‘yes’

27. Classification Process Step 2: Use the Model in Prediction
Classifier
Testing
Data
Unseen Data
(Jeff, Professor, 4)
Tenured?

28. Classification by Decision Tree Induction
A flow-chart-like tree structure
Internal node denotes a test on an attribute
Branch represents an outcome of the test
Leaf nodes represent class labels or class distribution
Decision tree generation consists
Tree construction
At start, all the training examples are at the root
Examples are recursively partitioned based on selected attributes
Use of decision tree: Classifying an unknown sample

29. Training Dataset
This follows an example from Quinlan’s ID3

30. Tree Output: A Decision Tree for Credit Approval
age?
<=30
overcast
30..40
>40
student?
yes
credit rating? no
yes
excellent
fair no
yes
yes no

31. Extracting Classification Rules from Trees
Represent the knowledge in the form of IF-THEN rules
One rule is created for each path from the root to a leaf
Each attribute-value pair along a path forms a conjunction
The leaf node holds the class prediction
Rules are easier for humans to understand
Example
IF age = “<=30” AND student = “no” THEN buys_computer = “no”
IF age = “<=30” AND student = “yes” THEN buys_computer = “yes”
IF age = “31…40” THEN buys_computer = “yes”
IF age = “>40” AND credit_rating = “excellent” THEN buys_computer = “yes”
IF age = “>40” AND credit_rating = “fair” THEN buys_computer = “no”

32. Decision Trees: Attribute Selection Methods
Information gain (ID3/C4.5)
ID3 - All attributes are assumed to be categorical
C4.5 - Modified ID3 for continuous attributes

33. Decision Tree) Concept of Decision Tree
Tree-like graph for classification purpose분류
Through recursive partitioning it consists of root node, internal nodes, link, leaf

34. An Example of ‘Car Buyers’no
Job
M/F
Area
Age
Y/N 1 NJ M N 35 2 F 51 3 OW 31 Y 4 EM 38 5 S 33 6 54 7 49 8 32 9 10 11 12 50 13 36 14
* (a,b,c) means
a: total # of records, b: ‘N’ counts, c: ‘Y’ counts

35. Lab on Decision Tree(1) SPSS Clementine, SAS Enterprise Miner
See5/C5.0Download See5/C
Evaluation from

36. Lab on Decision Tree(2)
From below initial screen, choose File – Locate Data

37. Lab on Decision Tree(3)
Select housing.data from Samples folder and click open.

38. Lab on Decision Tree(3(4)
This data set is on deciding house price in Boston area. It has 350 cases and 13 variables.

39. Lab on Decision Tree (5) Input variables crime rate
proportion large lots: residential space
proportion industrial: ratio of commercial area
CHAS: dummy variable
nitric oxides ppm: polution rate in ppm
av rooms per dwelling: # of room for dwelling
proportion pre-1940
distance to employment centers: distance to the center of city
accessibility to radial highways: accessibility to high way
property tax rate per $10\,000
pupil-teacher ratio: teachers’ rate
B: racial statistics
percentage low income earners: ratio of low income people
Decision variable
Top 20%, Bottom 80%

40. Lab on Decision Tree(6)
For the analysis, click Construct Classifier or click Construct Classifier from File menu

41. Lab on Decision Tree(7)
Click on Global pruning to (V ). Then, click OK

42. Lab on Decision Tree(8) Decision Tree Evaluation with Training data
Evaluation with Test data

43. Lab on Decision Tree(9) Understanding picture
We can see that (av rooms per dwelling) is the most important variable in deciding house price.

44. Lab on Decision Tree(11) 의사결정나무 그림으로는 규칙을 알아보기 어렵다.
To view the rules, close current screen and click Construct Classifier again or click Construct Classifier from File menu.

45. Lab on Decision Tree(12)
Choose/click Rulesets. Then click OK.

46. Lab on Decision Tree(13)