Data Mining with Clementine

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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?

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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

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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 rooma number of companies werent
  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)

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Applications Retail

• Use data mining to understand customers wants,
  needs, and preferences
• Based on this information, deliver timely,
  personalized promotional offers

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Applications Insurance

• Leverage data and text mining to speed claims
  processing and help reduce fraud

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Applications Manufacturing

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

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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

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Metaphor Data Mining and Gold Mining
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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

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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

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Deal 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?vnRKzseCLja8
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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

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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/capmus
ic/the_wild_west/gold_rush/clemtine.mid
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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

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Clementine Basics Building a Model
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Clementine Basics Select a Data Source
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Clementine Basics Select a Data File
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Clementine Basics Select a Data File
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Clementine Basics Read a Data File
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Clementine Basics Select Fields
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Clementine Basics Define Field Types
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Clementine Basics Visualize Data

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

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Clementine Basics Visualize Data

• Examine associations among variables using visual
  displays

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Clementine Basics Select Target and Predictors
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Clementine Basics Execute Model
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Clementine Basics Review Model Results
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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
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A Typical Clementine Model
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Modeling Approaches

• But can also use expert capabilities (advanced
  user)

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

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Data Mining Procedures

• Estimation
• Prediction
• Classification
• Clustering
• Affinity/Association

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Specific Methodologies Available

• Estimation Prediction
• - Neural networks
• Classification
• - Decision trees (2 types)

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Specific Methodologies Available

• Clustering
• - K-means
• - Kohonen networks
• Affinity/Association
• - Association rules (2 types)

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Positioning the Course
Business Applications
Theory and Concepts
Clementine Models

Focus of the Course
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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)

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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

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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
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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
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• 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

• 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

• 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

• 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
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• 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 gt 1

confidence p (consequent)
From Data Mining Techniques by Michael J. A.
Berry and Gordon S. Linoff
45

• Build a Clementine Model

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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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Instructors Laptop Screen
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Students Laptop Screen
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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

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Thank you for your time and participation

• Questions?
• Additional Information Please see my syllabus at
  http//www.spss.com/academic/educator/curriculum/i
  ndex.htm?tab1
• Comments and suggestions are welcome. Please send
  them to Girish.Punj_at_business.uconn.edu