data mining with clementine girish punj professor of marketing school of business university of...

Data Mining with Clementine

Girish PunjProfessor of MarketingSchool of BusinessUniversity of Connecticut

How to introduce data mining to students

Why Clementine?

Clementine features and capabilities

A typical data mining class

Useful teaching resources

Questions?

AgendaAgenda

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 StudentsIntroduce 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’)

Introduce Data Mining to StudentsIntroduce Data Mining to Students

Applications: RetailApplications: Retail

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

Based on this information, deliver timely, personalized promotional offers

Applications: InsuranceApplications: Insurance

Leverage data and text

mining to speed claims

processing and help

reduce fraud

Applications: Applications: ManufacturingManufacturing

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

Applications: TelecomApplications: 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

Metaphor: Data Mining and Gold MiningMetaphor: Data Mining and Gold Mining

Data Mining and Knowledge DiscoveryData 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 StatisticsData 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

Deal with NumerophobiaDeal with Numerophobia

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

Use a math primer for numerically challenged students

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

Introduce Software to StudentsIntroduce 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?

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…”

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

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

Introduce Software to StudentsIntroduce Software to Students

Clementine Basics: Building a ModelClementine Basics: Building a Model

Clementine Basics: Select a Data SourceClementine Basics: Select a Data Source

Clementine Basics: Select a Data File

Clementine Basics: Read a Data File

Clementine Basics: Select Fields

Clementine Basics: Define Field Types

Clementine Basics: Visualize DataClementine Basics: Visualize Data

Create tables and charts for means, ranges, and correlations of all variables

Clementine Basics: Visualize Data

Examine associations among variables using visual displays

Clementine Basics: Clementine Basics: Select Target and PredictorsSelect Target and Predictors

Clementine Basics: Execute Model

Clementine Basics: Review Model ResultsClementine Basics: Review Model Results

Models

Up sell/ Cross sell

Customer Churn

Propensity to respond/purchase

Creating business rules for Up sell & Cross Sell

Identify and target likely churn candidates, and create retention offerings to decrease their likelihood to churn

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

Building Models in ClementineBuilding Models in Clementine

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A Typical Clementine ModelA Typical Clementine Model

Modeling ApproachesModeling Approaches

Can use auto “c.h.d” settings (beginning user)

But can also use expert

capabilities (advanced user)

Data Mining ProceduresData Mining Procedures

Estimation

Prediction

Classification

Clustering

Affinity/Association

Specific Methodologies AvailableSpecific Methodologies Available

Estimation & Prediction:

- Neural networks

Classification:

- Decision trees (2 types)

Specific Methodologies AvailableSpecific Methodologies Available

Clustering:

- K-means

- Kohonen networks

Affinity/Association:

- Association rules (2 types)

Theory andConcepts

BusinessApplications

Clementine Models Focus of the

Course

Positioning the CoursePositioning the Course

A Typical ClassA 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 ApplicationsDiscuss 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

Where should detergents be placed in the Store to maximize their sales?

? Are window cleaning products also purchased when detergents and orange juice are bought together?

?

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 ConceptsPresent Theory and Concepts


Start with a record of past purchase transactions that link items purchased together

Customer Items

1 orange juice, soda2 milk, orange juice, window cleaner3 orange juice, detergent4 orange juice, detergent, soda5 window cleaner, soda

Purchase Transactions


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

Co-ocurrence Matrix

OJWindow Cleaner

Milk Soda Detergent

OJ 4 1 1 2 1Window Cleaner 1 2 1 1 0Milk 1 1 1 0 0Soda 2 1 0 3 1Detergent 1 0 0 1 2

The co-occurrence matrix shows the number of timesthe “row” item was purchased with the “column” item (note that the matrix is symmetrical)



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%

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%

Customer Items Purchased

1 OJ, soda

2 Milk, OJ, window cleaner

3 OJ, detergent

4 OJ, detergent, soda

5 Window cleaner, soda



Antecedent

OJ 45 %Soda 42.5 %Chips 40 %OJ and Soda 25 %OJ and Chips 20 %Soda and Chips 15 %OJ and Soda and Chips 5 %

Probability

Support (Prevalence): Percentage of records

in the dataset that match the antecedent Support = p (antecedent)



Rule p(anteced.)p(anteced.

and consequent)

confidence

If OJ and Soda, then Chips 25% 5% 0.20If OJ and Chips, then Soda 20% 5% 0.25If Soda and Chips, then OJ 15% 5% 0.33

Confidence (Predictability): Percentage of records in the

dataset that match the antecedent and also match the

consequent

Confidence =p (antecedent and consequent)

p (antecedent)



Rule confidence p(consequent) lift

If OJ and Soda then Chips 20% 40.0% 0.50If OJ and Chips then Soda 25% 42.5% 0.59If Soda and Chips then OJ 33% 45.0% 0.73If OJ then Soda 56% 42.5% 1.31

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

confidencep (consequent)



Build a Clementine ModelBuild a Clementine Model

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.

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Instructor’s Laptop ScreenInstructor’s Laptop Screen

Student’s Laptop ScreenStudent’s Laptop Screen

ResourcesResources

“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 participationThank 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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