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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
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: Visualize DataClementine Basics: Visualize Data
Create tables and charts for means, ranges, and correlations of all variables
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
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
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
From: Data Mining Techniques by Michael J. A. Berry and Gordon S. Linoff
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)
From: Data Mining Techniques by Michael J. A. Berry and Gordon S. Linoff
Present Theory and ConceptsPresent Theory and Concepts
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
From: Data Mining Techniques by Michael J. A. Berry and Gordon S. Linoff
Present Theory and ConceptsPresent Theory and Concepts
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
From: Data Mining Techniques by Michael J. A. Berry and Gordon S. Linoff
Present Theory and ConceptsPresent Theory and Concepts
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)
From: Data Mining Techniques by Michael J. A. Berry and Gordon S. Linoff
Present Theory and ConceptsPresent Theory and Concepts
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)
From: Data Mining Techniques by Michael J. A. Berry and Gordon S. Linoff
Present Theory and ConceptsPresent Theory and Concepts
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)
From: Data Mining Techniques by Michael J. A. Berry and Gordon S. Linoff
Present Theory and ConceptsPresent Theory and Concepts
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.
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]