Issues in Data Mining Applications Tutorial

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Issues in Data Mining Applications-Tutorial-
How to Make A Decision About Your Own Data Mining
Tool?
Authors

• Nemanja Jovanovic, nemko_at_sezampro.yu
• Valentina Milenkovic, tina_at_eunet.yu
• Prof. Dr. Veljko Milutinovic, vm_at_etf.bg.ac.yu

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Data Mining vs. Knowledge Mining ?

• ?

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Instead of a foreword

• .If you are not able
• to swim in the ocean of the data,
• you will get drowned.

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Tutorial Content
This Tutorial will guide you through the
following sections

• What really means Data Mining?
• Successful Data Mining
• Comparasion of fourteen DM tools
• How to improve existing Data Mining applications?
• Potential applications
• Myths and facts about Data Mining
• Two case studies
• The future of DM applications

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Other definitions of Data Mining

• Data mining is the (semi)automatic discovery of
  patterns, associations, anomalies, and changes in
  data.
• Data mining, on the other hand, extracts
  information from a database that the user did
  not know existed.
• Also, data mining is the search for relationships
  and global
  patterns that exist in large databases
  but are
  hidden' among the vast amount of data.

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The Foundations of Data Mining

• Massive data collection
• Powerful multiprocessor computers
• Data Mining algorithms

Volume of data
1970
1980
1990
2000
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Evolution Of Data Mining
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Data Mining context

• Application domain
• Data mining problem type
• Technical aspect
• Data mining tools and technique

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Data Mining Techniques

• Artificial Neural Networks
• Decision Trees
• Genetic Algorithms
• Rule Induction
• K-Nearest Neighbor (k-NN)
• Data Visualization

. . .
Input patern
. . ..
Output patern
. . .
. . .
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What type of a user do DM tools require?

• cooperation between business and datamining
  experts
• less skill and experience business experts in
  modeling and using the tools ? more help they
  need from data mining experts
• example of financial analysts

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Examples of DM projects to stimulate your
imagination

• Here are six examples of how data mining is
  helping corporations to operate more
  efficiently and profitably in today's business
  environment.
• Targeting a set of consumers who are most
  likely to respond to a direct mail campaign
• Predicting the probability of default for
  consumer loan applications
• Reducing fabrication flaws in VLSI chips
• Predicting audience share for television programs
• Predicting the probability that a cancer patient
  will respond to radiation therapy
• Predicting the probability that an offshore oil
  well is actually going to produce oil

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Successful Data Mining

• Come up with a precise formulation of the problem
  you are trying to solve and use the right data
• Have a clearly articulated business problem and
  then determine whether data mining is the proper
  solution technology
• Understand and deliver the fundamentals
• Have your technology folks be involved, too
• Visualization of the data mining output is very
  important in a meaningful way
• Allow the user to interact with the visualization

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Comparison of forteen DM tools

• Evaluated by four undergraduates inexperienced at
  data mining, a relatively
  experienced graduate student and
  a profesional data mining
  consultant
• Run under the MS Windows 95, MS Windows NT,
  Macintosh System 7.5
• Use one of the four technologies
  Decision Trees, Rule
  Inductions, Neural or Polynomial Networks
• Solve two binary classification problems
  multi-class
  classification and noiseless estimation problem
• Price from 75 to 25.000

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Comparison of forteen DM tools

• The Decision Tree products were - CART
  - Scenario - See5 -
  S-Plus
• The Rule Induction tools were - WizWhy
  - DataMind - DMSK
• Neural Networks were built from three
  programs - NeuroShell2 - PcOLPARS
  - PRW
• The Polynomial Network tools were -
  ModelQuest Expert - Gnosis - a
  module of NeuroShell2 - KnowledgeMiner

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Criteria for evaluating DM tools

• A list of 20 criteria for evaluating DM tools,
  put into 4 categories
• Capability measures what a desktop tool can do,
  and how well it does
  it - Handless missing data -
  Considers misclassification costs - Allows
  data transformations - Quality of tesing
  options - Has programming language -
  Provides useful output reports -
  Visualisation

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Visualisation
? excellent capability ? good capability ?-
some capability blank no capability
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Criteria for evaluating DM tools

• Learnability/Usability shows how easy a tool is
  to learn and use - Tutorials -
  Wizards - Easy to learn - Users
  manual - Online help - Interface

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Criteria for evaluating DM tools

• Interoperability shows a tools ability to
  interface with other
  computer applications - Importing
  data - Exporting data - Links to
  other applications
• Flexibility - Model adjustment
  flexibility - Customizable work
  enviroment - Ability to write or change code

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Data Input Output Model
? excellent capability ? good capability ?-
some capability blank no capability
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A classification of data sets

• Pima Indians Diabetes data set
• 768 cases of Native American women from the Pima
  tribe some of whom are diabetic,
  most of whom are not
• 8 attributes plus the binary class variable for
  diabetes per instance
• Wisconsin Breast Cancer data set
• 699 instances of breast tumors some of which
  are malignant, most of which are benign
• 10 attributes plus the binary malignancy
  variable per case
• The Forensic Glass Identification data set
  
• 214 instances of glass collected during crime
  investigations
  
• 10 attributes plus the multi-class output
  variable per instance
• Moon Cannon data set
  
• 300 solutions to the equation x 2v 2
  sin(g)cos(g)/g
  
• the data were generated without adding noise

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Evaluation of forteen DM tools
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Strenghts and Weaknesses

• Strengths
• Ease of use (Scenario, WizWhy..)
• Data visualisation (S-plus,MineSet...)
  
• Depth of algorithms (tree options)
  (CART,See5,S-plus..)
• Multiplte neural network architectures
  (NeuroShell)

• Weaknesses
• Difficult file I/O (OLPARS,CART)
• Limited visualisation (PRW,See5,WizWhy)
• Narrow analyses path (Scenario)

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How to improve existing DM applications

• The top ten points
• Database integration
  
• no more flat files
• use the millions spent on data warehousing
  
  
• Automated model scoring
• without scoring DM is pretty useless
• should be integrated with the driving
  applications
• Exporting models to other applications
• close the loop between DM and applications
  that need to
  use the results (scores)

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How to improve existing DM applications

• Business templates
• cross-selling specific application is more
  valuable than a general
  modeling tool
  
• Effort knob
• it is relevant in a way that tuning parametars
  are not
• Incorporate financial information
• the financial information is very important and
  often available and shold be provided as
  input to the DM application

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How to improve existing DM applications

• Computed target columns
• allow the user to interactively create a new
  target variable
• Time-series data
• a years worth of monthly balance information is
  qualitatively different than twelve distinct
  non-time-series variables
• Use versus View
• do not present visually to user the full model,
  only the most
  important levels
• Wizards
• not necessarily but desirable
• prevent human error by keeping the user on track

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Potential Applications

• Data mining has many varied fields of
  application,
• some of which are listed below.
• Retail/Marketing
• Identify buying patterns from customers
  
• Find associations among customer demographic
  characteristics
• Predict response to mailing campaigns
• Market basket analysis

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Potential Applications

• Banking
• Detect patterns of fraudulent credit card use
• Identify loyal' customers
• Determine credit card spending by customer groups
  
• Find hidden correlations between different
  financial indicators
• Identify stock trading rules from historical
  market data

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Potential Applications

• Insurance and Health Care
• Claims analysis - i.e., which medical procedures
  are claimed together
• Predict which customers will buy new policies
• Identify behaviour patterns of risky customers
• Identify fraudulent behaviour

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Potential Applications

• Transportation
• Determine the distribution schedules among
  outlets
• Analyse loading patterns
• Medicine
• Characterise patient behaviour to predict office
  visits
• Identify successful medical therapies for
  different illnesses
• To predict the effectiveness of surgical
  procedures or
  medical tests

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Potential Applications

• Sport
• To make the best choice about players in
  different circumstance
• To predict the results of relevance match
• Do a better list of seed players in groups or
  tournament
• DM report from an NBA game
• When Price was Point-Guard, J.Williams missed 0
  (0) of his jump field-goal attempts and made
  100 (4) of his jump field-goal-attempts.
• The total number of such field-goal-attempts
  was 4.

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DM and Customer Relationship Management

• CRM is a process that manages the interactions
  between a company and its customers
• Users of CRM software applications are database
  marketers
• Goals of database marketers are
• identifying market segments, which requires
  significant data about prospective customers
  and their buying behaviors
• build and execute campaigns
• Tightly integrating the two disciplines presents
  an opportunity for companies to gain
  competetive adventage

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DM and Customer Relationship Management

• How Data Mining helps Database Marketing
• Scoring
• The role of Campaign Management Software
• Increasing the customer lifetime value
• Combining Data Mining and Campaign Management

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DM and Customer Relationship Management

• Evaluating the benefits of a Data Mining model

Gains chart
Profability chart
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Myths and Facts about Data Mining

• Myth DM produces surprising results
  that will utterly
  transform your business.
• Myth DM techniques are so sophisticated
  that they can
  substitute for domain knowledge or for
  experience in analysis and model building.
• Myth DM tools automatically find the patterns
  you are looking for,
  without being told what to do.

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Myths and Facts about Data Mining

• Myth Data mining is more effective with more
  data, so all existing data
  should be brought into any data-mining effort.
• Myth Building a DM model on a sample of a
  database is ineffective, because sampling loses
  the information in the unused data.
• Myth Data mining is another fad that will soon
  fade, allowing us to return to
  standard business practice.

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Myths and Facts about Data Mining

• Myth DM is useful only in certain areas,
  such as
  marketing, sales, and fraud detection.
• Myth The methods used in DM are fundamentally
  different from the older quantitative
  model-building techniques.
• Myth Data mining is an extremely complex
  process.
• Myth Only massive databases are worth mining.

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Data Mining Examples

• Bass Brewers Weve been brewing beer
  since 1777, with increased competition comes a
  demand to make faster better informed decision
• Northern Bank The information is now more
  accessible, paperless and timely.
• TSB Group Plc We are using Holos because
  of its flexibility and its excellent
  multidimensional database

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Data Mining Examples

• Delphic Universites Real value is added to
  data by multidimensional manipulation (being
  able to to easily compare many different views
  of the avaible information in one report) and by
  modeling.
• Harvard - Holden Sybase technology has
  allowed us to develop an information system that
  will preserve this legacy into the twenty-first
  century
• J.P.Morgan The promise of data mining
  tools like Information Harvester is that they
  are able to quickly wade through massive amounts
  of data to identify relationships or trending
  information that would not have been avaible
  without the tool

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Case study of Breast Cancer Survival Analysis

• Case study of the influence of various patient
  characteristics on survival rates for breast
  cancer
• The survival analysis technique employed is Cox
  Regression (this technique is useful in
  situations, where some of the patients do not
  die during the observation period)
• Linear regression technique (if all
  patients had died during the observation period)

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Case study of Breast Cancer Survival Analysis

• The observation period runs for 133.8 months
• The modeling sample contains 746 patients (50
  patients died during the observation period and
  696 who survived beyond the end of the
  observation period)
• In this example, we are testing only four
  predictors
• Age, in years, at the start of the observation
  period (22 to 88)
• Pathological tumor size, in centimeters (0.10 to
  7.00)
• Number of positive axillary lymph nodes (0 to 35)
• Estrogen receptor status (positive vs. negative)

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Case study of Breast Cancer Survival Analysis

• The Cox Regression used a backward stepwise
  likelihood-ratio variable selection method
• Significance criteria were set at 0.05 for
  inclusion in the model, and 0.10 for
  removal from the model
• Printout from the final step of the stepwise
  regression analysis
• ________________ Variables in the Equation
  ______________
• Variable B S.E. Wald
  df Sig R Exp(B)
• AGE -.0314 .0121 6.7486
  1 .0094 -.0893 .9691
• PATHSIZE .3975 .1175 11.4476 1
  .0007 .1259 1.4881
• LNPOS .1372 .0361 14.4100 1
  .0001 .1443 1.1471
• __________________________________________________
  _____
• The column labeled "Sig" shows the statistical
  significance of included variables
• The column labeled "R" shows the degree of
  unique correlation with the dependent variable

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Case study of Breast Cancer Survival Analysis

• Some key things to note are
• Estrogen status was removed as a predictor
  because it did not reach the 0.05
  significance criterion for inclusion
• Number of positive axillary lymph nodes was the
  strongest predictor of survival rates (R.1443 /
  Sig.0001), then follow pathological tumor
  size (R.1259 / Sig..0007), over the course of
  the observation period
• Age, although significant, is somewhat less
  influential than the other two predictors
  (R-0.893 / Sig..0094)
• Note that both the number of positive axillary
  lymph nodes and the pathological tumor size
  are positively correlated, which means that they
  are directly associated with more rapid
  mortality.
• Age is negatively correlated with the dependent
  variable, which means that younger age is
  predictive of somewhat longer survival.

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Case study of Breast Cancer Survival Analysis

• All patients survive through the 10 month of
  the observation period
• At the fortieth month, the mortality
  rate increases and continues at this fairly
  constant increased rate
  through the forty-fifth month
• At the forty-fifty month, there is a
  five-month period without additional mortality
• 11 of the original sample has died

The following chart shows the cumulative
survival function during the observation period
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Case study of Breast Cancer Survival Analysis

• Conclusions and Implications
• The case study presented here is relatively
  simple, and is for illustrative purposes only.
• With the addition of more candidate predictors
  (progesterone receptor status, histologic
  grade, blood type etc.), an even more powerful
  model could emerge.
• By understanding the influence of patient
  characteristics on mortality rates over time, we
  are in a better position to estimate survival
  times for individual patients, and to defend
  using different or more aggressive therapeutic
  approaches for some patients.

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Securities Brokerage Case Study

• Predictive market segmentation model designed to
  identify and profile high-value brokerage
  customer segments as targets for special
  marketing communications efforts.
• The dependent variable for this ordinal CHAID
  model is brokerage account
  commission dollars during the past 12 months
• We begin by splitting the client's entire
  customer file into a modeling sample and a
  validation sample. (Once the model is built
  using the modeling sample, we apply it to
  the validation sample to see how well it works
  on a sample other than the one on which it
  was built).

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Securities Brokerage Case Study

• The resulting CHAID model has 55 segments.
• However, the results are summarized in the
  following comb chart, showing the segment indexes
  (indexes of average dollar value)

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Securities Brokerage Case Study
The part of Gains Chart Average Annual Brokerage
Commission Dollars

• Gains chart provides quantitative detail useful
  for financial and marketing planning.
• We have highlighted the top 20 of the file in
  blue
• The top 20 of the file is worth an average
  of about 334 per account, which is
  nearly three times the average account value for
  the entire sample.

...
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Securities Brokerage Case Study

• Using the data in the gains chart this
  information, we can better plan our
  communications/promotion budget.
• In general, the best segments represent customers
  who are experienced, aggressive, self-directed
  traders.
• The other decisions, which the gains chart and
  the segmentation rules can help us make
• We might wish to conduct some market research
  among customers in
  under-performing segments, or among
  under-performing customers in the
  better segments
• We can use the segment definitions to help us
  identify possible issues and question areas to
  include in the survey
• Before we try to apply such a model, we perform a
  validation against a holdout sample, to confirm
  that it is a good model.

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The future of DM applications

• Different opinions
• Very little functionality in DB systems to
  support DM applications
• Data mining, as a vital application, is
  just one more advance in the on-going research
  process
• Data mining will not go away

T h e E n d