STA 7923 BioinformaticsII Data Mining

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STA 7923 BioinformaticsII Data Mining

• Daijin Ko
• MMS, UTSA
• Spring, 2008

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Course

• Instructor
• Daijin Ko
• BB 4.03.18
• daijin.ko_at_utsa.edu
• MW 4-515 PM
• OH MW 100-145 PM or appointment

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Grading

• Homework 20
• Project 20
• Midterm I and II 40
• Final Exam 20

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Project

• Data Mining Project related to course subject
  matter.
• We will provide some databases to mine welcome
  to use your own data.

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Team Projects

• Working in pairs OK, but
• We will expect more from a pair than from an
  individual.
• The effort should be roughly evenly distributed.

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Course Outline

• Introduction
• What is data mining?
• Examples
• Supervised Learning
• Linear Regression and Classification models
• R program
• Model Assessment
• Smoothing and Generalized Additive models
• Classification and Regression Tree
• Neural Network
• Bagging and Boosting
• Random Forest and Support Vector Machine

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Course Outline (continued)

• Unsupervised Learning
• Clustering
• Association Rules
• Applications to Microarray Data

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What is Data Mining?

• Discovery of useful, possibly unexpected,
  patterns in data.
• Subsidiary issues
• Data cleansing detection of bogus data.
• E.g., age 150.
• Visualization something better than megabyte
  files of output.
• Warehousing of data (for retrieval).

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Typical Kinds of Patterns

• Decision trees succinct ways to classify by
  testing properties.
• Clusters another succinct classification by
  similarity of properties.
• Bayes, hidden-Markov, and other statistical
  models, frequent-itemsets expose important
  associations within data.

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Supervised vs. Unsupervised Learning

• Supervised Problem solving
• Driven by a real business problems and historical
  data
• Quality of results dependent on quality of data
• Unsupervised Exploration (aka clustering)
• Relevance often an issue
• Beer and baby diapers (who cares?)
• Useful when trying to get an initial
  understanding of the data
• Non-obvious patterns can sometimes pop out of a
  completed data analysis project

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Example Clusters
x xx x x x x x x x x x x x
x
x x x x x x x x x x x x
x x x
x x x x x x x x x x
x
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Clustering of Gene Markers

• Patients clustered based on survival

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Goal of Data Mining

• Simplification and automation of the overall
  statistical process, from data source(s) to model
  application
• Changed over the years
• Replace statistical models ? Better models, less
  grunge work
• Many different data mining algorithms / tools
  available
• Statistical expertise required to compare
  different techniques
• Build intelligence into the software

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Related Fields
Machine Learning
Visualization

Data Mining/ Knowledge Discovery
Statistics
Databases
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Cultures

• Databases concentrate on large-scale
  (non-main-memory) data.
• AI (machine-learning) concentrate on complex
  methods.
• Statistics concentrate on inferring models.

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Applications

• Science
• astronomy, bioinformatics, drug discovery,
• Business
• advertising, CRM (Customer Relationship
  management), investments, manufacturing,
  sports/entertainment, telecom, e-Commerce,
  targeted marketing, health care,
• Web
• search engines, bots,
• Government
• law enforcement, profiling tax cheaters,
  anti-terror(?)

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Why Data Mining? Why not Statistics?

• Data Flood
• Bank, telecom, other business transactions ...
• Scientific data astronomy, biology, etc
• Web, text, and e-commerce
• Computer Power
• Moores law doubles computing power every 18
  months
• Powerful workstations became common
• Cost effective servers (SMPs) provide parallel
  processing to the mass market
• Interesting tradeoff Small number of large
  analyses vs. large number of small analyses
• Improved Algorithms
• Techniques have often been waiting for computing
  technology to catch up
• Statisticians already doing manual data mining
• Good machine learning is just the intelligent
  application of statistical processes

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Big Data Examples

• Europe's Very Long Baseline Interferometry (VLBI)
  has 16 telescopes, each of which produces 1
  Gigabit/second of astronomical data over a 25-day
  observation session
• storage and analysis a big problem
• ATT handles billions of calls per day
• so much data, it cannot be all stored -- analysis
  has to be done on the fly, on streaming data

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Largest databases in 2003

• Commercial databases
• Winter Corp. 2003 Survey France Telecom has
  largest decision-support DB, 30TB ATT 26 TB
• Web
• Alexa internet archive 7 years of data, 500 TB
• Google searches 4 Billion pages, many hundreds
  TB
• IBM WebFountain, 160 TB (2003)
• Internet Archive (www.archive.org), 300 TB

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Data Growth Rate

• Twice as much information was created in 2002 as
  in 1999 (30 growth rate)
• Other growth rate estimates even higher
• Very little data will ever be looked at by a
  human
• Data Mining is NEEDED to make sense and use of
  data.

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Meaningfulness of Answers

• A big risk when data mining is that you will
  discover patterns that are meaningless.
• Statisticians call it Bonferronis principle
  (roughly) if you look in more places for
  interesting patterns than your amount of data
  will support, you are bound to find crap.

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Examples

• A big objection to TIA was that it was looking
  for so many vague connections that it was sure to
  find things that were bogus and thus violate
  innocents privacy.
• The Rhine Paradox a great example of how not to
  conduct scientific research.

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Rhine Paradox --- (1)

• David Rhine was a parapsychologist in the 1950s
  who hypothesized that some people had
  Extra-Sensory Perception.
• He devised an experiment where subjects were
  asked to guess 10 hidden cards --- red or blue.
• He discovered that almost 1 in 1000 had ESP ---
  they were able to get all 10 right!

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Rhine Paradox --- (2)

• He told these people they had ESP and called them
  in for another test of the same type.
• Alas, he discovered that almost all of them had
  lost their ESP.
• What did he conclude?
• Answer on next slide.

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Rhine Paradox --- (3)

• He concluded that you shouldnt tell people they
  have ESP it causes them to lose it.

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A Concrete Example

• This example illustrates a problem with
  intelligence-gathering.
• Suppose we believe that certain groups of
  evil-doers are meeting occasionally in hotels to
  plot doing evil.
• We want to find people who at least twice have
  stayed at the same hotel on the same day.

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The Details

• 109 people being tracked.
• 1000 days.
• Each person stays in a hotel 1 of the time (10
  days out of 1000).
• Hotels hold 100 people (so 105 hotels).
• If everyone behaves randomly (I.e., no
  evil-doers) will the data mining detect anything
  suspicious?

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Calculations --- (1)

• Probability that persons p and q will be at the
  same hotel on day d
• 1/100 1/100 10-5 10-9.
• Probability that p and q will be at the same
  hotel on two given days
• 10-9 10-9 10-18.
• Pairs of days
• 5105.

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Calculations --- (2)

• Probability that p and q will be at the same
  hotel on some two days
• 5105 10-18 510-13.
• Pairs of people
• 51017.
• Expected number of suspicious pairs of people
• 51017 510-13 250,000.

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Conclusion

• Suppose there are (say) 10 pairs of evil-doers
  who definitely stayed at the same hotel twice.
• Analysts have to sift through 250,010 candidates
  to find the 10 real cases.
• Not gonna happen.
• But how can we improve the scheme?

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Data Mining for Customer Modeling

• Customer Tasks
• attrition prediction
• targeted marketing
• cross-sell, customer acquisition
• credit-risk
• fraud detection
• Industries
• banking, telecom, retail sales,

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Case Study I

• Situation Attrition rate at for mobile phone
  customers is around 25-30 a year!
• Task
• Given customer information for the past N months,
  predict who is likely to attrite next month.
• Also, estimate customer value and what is the
  cost-effective offer to be made to this customer.

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Results

• Verizon Wireless built a customer data warehouse
  
• Identified potential attriters
• Developed multiple, regional models
• Targeted customers with high propensity to accept
  the offer
• Reduced attrition rate from over 2/month to
  under 1.5/month (huge impact, with gt30 M
  subscribers)
• (Reported in 2003)

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Case Study II Assessing Credit Risk

• Situation Person applies for a loan
• Task Should a bank approve the loan?
• Note People who have the best credit dont need
  the loans, and people with worst credit are not
  likely to repay. Banks best customers are in
  the middle

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Results

• Banks develop credit models using variety of
  machine learning methods.
• Mortgage and credit card proliferation are the
  results of being able to successfully predict if
  a person is likely to default on a loan
• Widely deployed in many countries

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Successful e-commerce Case Study

• A person buys a book (product) at Amazon.com.
• Task Recommend other books (products) this
  person is likely to buy
• Amazon does clustering based on books bought
• customers who bought The Elements of Statistical
  Learning, also bought Data Mining Practical
  Machine Learning Tools and Techniques with Java
  Implementations
• Recommendation program is quite successful

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Unsuccessful e-commerce case study (KDD-Cup 2000)

• Data clickstream and purchase data from
  Gazelle.com, legwear and legcare e-tailer
• Q Characterize visitors who spend more than 12
  on an average order at the site
• Dataset of 3,465 purchases, 1,831 customers
• Very interesting analysis by Cup participants
• thousands of hours - X,000,000 (Millions) of
  consulting
• Total sales -- Y,000
• Obituary Gazelle.com out of business, Aug 2000

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Genomic Microarrays Case Study

• Given microarray data for a number of samples
  (patients), can we
• Accurately diagnose the disease?
• Predict outcome for given treatment?
• Recommend best treatment?

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Example ALL/AML data

• 38 training cases, 34 test, 7,000 genes
• 2 Classes Acute Lymphoblastic Leukemia (ALL) vs
  Acute Myeloid Leukemia (AML)
• Use train data to build diagnostic model

Results on test data 33/34 correct, 1 error may
be mislabeled
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Security and Fraud Detection - Case Study

• Credit Card Fraud Detection
• Detection of Money laundering
• FAIS (US Treasury)
• Securities Fraud
• NASDAQ KDD system
• Phone fraud
• ATT, Bell Atlantic, British Telecom/MCI
• Bio-terrorism detection at Salt Lake Olympics 2002

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Problems Suitable for Data-Mining

• require knowledge-based decisions
• have a changing environment
• have sub-optimal current methods
• have accessible, sufficient, and relevant data
• provides high payoff for the right decisions!
• Privacy considerations important if personal data
  is involved

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Many Names of DM

• Data Fishing, Data Dredging 1960-
• used by Statistician (as bad name)
• Data Mining 1990 --
• used DB, business
• in 2003 bad image because of TIA
• (Total "Terrorism" Information Awareness)
• Knowledge Discovery in Databases (1989-)
• used by AI, Machine Learning Community
• also Data Archaeology, Information Harvesting,
  Information Discovery, Knowledge Extraction, ...

Currently Data Mining and Knowledge Discovery
are used interchangeably
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Good Names

• Machine Learning
• Statistical Learning
• Knowledge Discovery
• Data Mining

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Summary

• Technology trends lead to data flood
• data mining is needed to make sense of data
• Data Mining has many applications, successful and
  not
• Data Mining Tasks
• classification, clustering,