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Data Mining with Clementine Girish Punj Professor of Marketing School of Business University of Connecticut Agenda How to introduce data mining to students Why Clementine? Clementine features and capabilities A typical data mining class Useful teaching resources

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data mining with clementine

Data Mining with Clementine

Girish Punj

Professor of Marketing

School of Business

University of Connecticut

agenda
Agenda
  • How to introduce data mining to students
  • Why Clementine?
  • Clementine features and capabilities
  • A typical data mining class
  • Useful teaching resources
  • Questions?
introduce data mining to students
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
introduce data mining to students4
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’)

applications retail
Use data mining to understand customers’ wants, needs, and preferences

Based on this information, deliver timely, personalized promotional offers

Applications: Retail
applications manufacturing
Applications: Manufacturing

Model historical production and quality data to reduce development time and improve quality of production processes

applications telecom
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

data mining and knowledge discovery
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
data mining and statistics
Data Mining

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

Data Mining and Statistics
deal with numerophobia
Deal with Numerophobia

http://www.youtube.com/watch?v=nRKzseCLja8

Emphasize Differences between Statistics and Data Mining to advantage (no probability distributions)

Use a math primer for numerically challenged students

introduce software to students
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”
who was clementine
Who was Clementine?

http://www.empire.k12.ca.us/capistrano/mike/capmusic/the_wild_west/gold_rush/clemtine.mid

Daughter of a miner during the 1849 California Gold Rush who developed a reputation…

“In a cavern, in a canyon,Excavating for a mineDwelt a miner, forty niner,And his daughter Clementine…”

introduce software to students15
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
building models in clementine
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

Propensity to respond/purchase

Customer Churn

Develop models on desired purchase behavior, and target candidates that are most likely to respond

modeling approaches
Can use auto “c.h.d” settings (beginning user)Modeling Approaches
  • But can also use expert capabilities (advanced user)
data mining procedures
Estimation

Prediction

Classification

Clustering

Affinity/Association

Data Mining Procedures
specific methodologies available
Estimation & Prediction:

- Neural networks

Classification:

- Decision trees (2 types)

Specific Methodologies Available
specific methodologies available33
Specific Methodologies Available
  • Clustering:

- K-means

- Kohonen networks

  • Affinity/Association:

- Association rules (2 types)

positioning the course
Positioning the Course

Business

Applications

Theory and

Concepts

Clementine Models

Focus of the

Course

a typical class
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)
discuss business applications
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

present theory and concepts
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

present theory and concepts38
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

slide39
Create a co-occurrence matrix that pairs items purchased together in the form of a table

Present Theory and Concepts

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

slide40
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%

Present Theory and Concepts

From: Data Mining Techniques

by Michael J. A. Berry and Gordon S. Linoff

slide41
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%

Present Theory and Concepts

From: Data Mining Techniques

by Michael J. A. Berry and Gordon S. Linoff

slide42

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

slide43

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

slide44

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

homework assignment
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.
resources
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
  • www.kdnuggets.com
thank you for your time and participation
Thank you for your time and participation
  • Questions?
  • Additional Information: Please see my syllabus at http://www.spss.com/academic/educator/curriculum/index.htm?tab=1
  • Comments and suggestions are welcome. Please send them to: [email protected]
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