1. Data Mining with Clementine
Girish Punj
Professor of Marketing
School of Business
University of Connecticut

2. Agenda How to introduce data mining to students Why Clementine?
Clementine features and capabilities
A typical data mining class
Useful teaching resources
Questions?

3. Introduce Data Mining to Students
Data mining chosen as one of top 10 emerging technologies..” (MIT Technology Review)
Data mining expertise is most sought after...” (Information Week Survey)
Data mining skills are an important part of the “toolkit” needed by managers in a complex business world
Data Mining for job advancement and as career insurance during good and bad economic times

4. Introduce Data Mining to Students
“When I looked at what companies were doing with
analytics I found it had moved from the back room to
the board room…a number of companies weren’t just
using analytics, they were now competing on
analytics -- they had made analytics the central strategy
of their business.”
(Tom Davenport, author of ‘Competing on Analytics’)
“We are drowning in information but starved for
knowledge.”
(John Naisbitt author of ‘Megatrends’)

5. Applications: Retail
Use data mining to understand customers’ wants, needs, and preferences
Based on this information, deliver timely, personalized promotional offers

6. Applications: Insurance
Leverage data and text mining to speed claims processing and help reduce fraud

7. Applications: Manufacturing
Model historical production and quality data to reduce development time and improve quality of production processes

8. Applications: Telecom
Use data mining to identify appropriate customer segments for new marketing initiatives
Predict likelihood of customer churn and target those likely to leave with retention campaigns

9. Metaphor: Data Mining and Gold Mining

10. Data Mining and Knowledge Discovery
Data mining is the process of discovery of interesting, meaningful and actionable patterns hidden in large amounts of data (Han and Kamber 2006)
Knowledge Discovery (KD) as a more inclusive term
Knowledge Discovery using a combination of artificial and human intelligence
Data → Information → Knowledge

11. Data Mining and Statistics
No hypotheses are needed
Can find patterns in very large amounts of data
Uses all the data available
Terminology used: field, record, supervised learning, unsupervised learning
Statistics
Uses Hypothesis testing
Techniques are not suitable for large datasets
Relies on sampling
Terminology used: variable, observation, analysis of dependence, analysis of interdependence

12. Deal with Numerophobia
SPSS Inc.
Deal with Numerophobia
Emphasize Differences between Statistics and Data Mining to advantage (no probability distributions)
Use a math primer for numerically challenged students
Copyright , SPSS Inc. 12

13. Introduce Software to Students
Clementine 12.0:
Student Version (Clementine GradPack) is of enterprise strength
Student License extends for about eight months beyond course completion date
Directly address cost concerns by discussing value of “investment”

14. Who was Clementine?
Daughter of a miner during the 1849 California Gold Rush who developed a reputation…
“In a cavern, in a canyon, Excavating for a mine Dwelt a miner, forty niner, And his daughter Clementine…”

15. Introduce Software to Students
Visual approach makes model building an art form
Concept of “data flow” enables building of multiple models
Point-and-click model building (no manual coding)
Comprehensive portfolio of models for the Business Analyst as well as the Technical Expert

16. Clementine Basics: Building a Model

17. Clementine Basics: Select a Data Source
Adding a node in Clementine is relatively simple: just select the node you want from the palette menus and drag and drop it on to the canvas.

18. Clementine Basics: Select a Data File

19. Clementine Basics: Select a Data File

20. Clementine Basics: Read a Data File

21. Clementine Basics: Select Fields

22. Clementine Basics: Define Field Types

23. Clementine Basics: Visualize Data
Create tables and charts for means, ranges, and correlations of all variables

24. Clementine Basics: Visualize Data
Examine associations among variables using visual displays

25. Clementine Basics: Select Target and Predictors

26. Clementine Basics: Execute Model

27. Clementine Basics: Review Model Results

28. Building Models in Clementine
Up sell/ Cross sell
Creating business rules for Up sell & Cross Sell
Identify and target likely churn candidates, and create retention offerings to decrease their likelihood to churn
Models
Customer Churn
Propensity to respond/purchase
Develop models on desired purchase behavior, and target candidates that are most likely to respond

29. A Typical Clementine Model

30. Modeling Approaches
But can also use expert capabilities (advanced user)
Can use auto “c.h.d” settings (beginning user)

31. Data Mining Procedures
Estimation
Prediction
Classification
Clustering
Affinity/Association

32. Specific Methodologies Available
Estimation & Prediction:
- Neural networks
Classification:
- Decision trees (2 types)

33. Specific Methodologies Available
Clustering:
- K-means
- Kohonen networks
Affinity/Association:
- Association rules (2 types)

34. Positioning the Course
Business
Applications
Theory and
Concepts
Clementine Models
Focus of the
Course

35. A Typical Class
Discuss business applications of methodology based on brief articles from the business press (30 minutes)
Present theory and concepts (30 minutes)
Build a Clementine model for students (30 minutes)
Ask students build a Clementine model (30 minutes)
Discuss homework assignment (15 minutes)
Students complete a homework assignment after class (requires three hours)

36. Discuss Business Applications
“Wal-Mart's next competitive weapon is advanced data mining, which it will use to forecast, replenish and merchandise on a micro scale
By analyzing years' worth of sales data--and then cranking in variables such as the weather and school schedules--the system could predict the optimal number of cases of Gatorade, in what flavors and sizes, a store in Laredo, Texas, should have on hand the Friday before Labor Day
Then, if the weather forecast suddenly called for temperatures 5 hotter than last year, the delivery truck would automatically show up with more”
From: “Can Wal-Mart Get Any Bigger,” Time, 13 January, 2003

37. Present Theory and Concepts?
Are window cleaning products also purchased when detergents and orange juice are bought together? ?
Where should detergents be placed
in the Store to maximize their sales?
Is soda typically purchased with
bananas? Does the brand of soda
make a difference? ?
How are the demographics of
the neighborhood affecting what
Customers are buying? ?
From: Data Mining Techniques
by Michael J. A. Berry and Gordon S. Linoff

38. Present Theory and Concepts
Start with a record of past purchase transactions that link items purchased together
From: Data Mining Techniques
by Michael J. A. Berry and Gordon S. Linoff

39. Present Theory and Concepts
Create a co-occurrence matrix that pairs items purchased together in the form of a table
The co-occurrence matrix shows the number of times
the “row” item was purchased with the “column” item (note that the matrix is symmetrical)
From: Data Mining Techniques
by Michael J. A. Berry and Gordon S. Linoff

40. Present Theory and Concepts
Customer
Items Purchased 1
OJ, soda 2
Milk, OJ, window cleaner 3
OJ, detergent 4
OJ, detergent, soda 5
Window cleaner, soda
Rule Support = Percentage of transactions with both the items of interest
What is the Support for the rule “If Soda, then OJ” ?
OJ and Soda are purchased together in 2 out of 5 transactions
Hence Support is 40%
What is the support for the rule “If OJ, then Soda” ?
Still 40%
From: Data Mining Techniques
by Michael J. A. Berry and Gordon S. Linoff

41. Present Theory and Concepts
Customer
Items Purchased 1
OJ, soda 2
Milk, OJ, window cleaner 3
OJ, detergent 4
OJ, detergent, soda 5
Window cleaner, soda
Confidence = Ratio of the number of transactions with both the items of interest to the number of transactions with the “If” items
What is the Confidence for “If Soda, then OJ” ?
2 out of 3 soda purchase transactions also include OJ
Hence Confidence is 66.66%
What is the Confidence for “If OJ, then Soda” ?
2 out of 4 OJ purchase transactions also include soda
Hence Confidence is 50%
From: Data Mining Techniques
by Michael J. A. Berry and Gordon S. Linoff

42. Present Theory and Concepts
Support (Prevalence): Percentage of records in the dataset that match the antecedent Support = p (antecedent)
From: Data Mining Techniques
by Michael J. A. Berry and Gordon S. Linoff

43. p (antecedent and consequent)
Present Theory and Concepts
Confidence (Predictability): Percentage of records in the dataset that match the antecedent and also match the consequent
Confidence =
p (antecedent and consequent)
p (antecedent)
From: Data Mining Techniques
by Michael J. A. Berry and Gordon S. Linoff

44. Present Theory and Concepts
Lift (Improvement): How much better a rule is at predicting the consequent than chance alone?
Lift =
A rule is only useful if Lift is > 1
confidence
p (consequent)
From: Data Mining Techniques
by Michael J. A. Berry and Gordon S. Linoff

45. Build a Clementine Model

46. Homework Assignment
Conduct a Market Basket Analysis on the dataset using both the Apriori and GRI modeling nodes in Clementine.
Reconcile the association rules discovered as a result of the Apriori and GRI modeling nodes.
Provide a narrative description that attempts to explain the convergence (or lack thereof) between the results obtained from the two modeling nodes. 
Select those association rules discovered during your Market Basket Analysis that would make the most intuitive sense to the category managers involved and create demographic profiles of shoppers who appear to fit those rules.

47. Instructor’s Laptop Screen

48. Student’s Laptop Screen

49. Resources
“Data Mining Techniques” by Michael J. A. Berry and Gordon S. Linoff (second edition), Wiley, 2004
“Discovering Knowledge in Data” by Daniel T. Larose, Wiley, 2005
“Making Sense of Statistics” by Fred Pyrczak (fourth edition), Pyrczak Publishing, 2006
Recent articles from the business press identified using the “Factiva” database and “data mining” “predictive analytics” as search keywords

50. Thank you for your time and participation
SPSS Inc.
Thank you for your time and participation
Questions?
Additional Information: Please see my syllabus at
Comments and suggestions are welcome. Please send them to:
Copyright , SPSS Inc.