Data Mining

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Data Mining

• Lecture 1

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Instructor Info

• Name Ertan Karakurt
• Contact ertankarakurt_at_akillisistemler.com.tr
• 10 years experience on Data Mining and
  Intelligent Applications Development
• General Purpose Data Mart Development for
  Financial Modeling
• Behavioral Clustering of Retail Customers in
  Banking Sector
• Propensity Modeling for Cross Selling
• Attrition/Retention Modeling
• Modeling Algorithms Library Development for
  Defense

...
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Instructor Info

• Ertan Karakurt
• founder of Izmir based Akilli Sistemler
• fuzzy/exact searching/matching engine for
  Databases
• search space analyzing, learning
• algorithm space analyzing, learning
• parallelization architecture

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Course Objective

• stimulate university and industry cooperation
• create an opportunity to work with real life
  applications and problems in Data Mining
• case studies on data dictionaries
• case studies on physically built data mining
  models
• adjusting/utilizing the balance point between
  theory and application in Data Mining

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Course Syllabus

• Course topics
• Introduction (Week1-Week2)
• What is Data Mining?
• Data Collection and Data Management Fundamentals
• The Essentials of Learning
• The Emerging Needs for Different Data Analysis
  Perspectives
• Data Management and Data Collection Techniques
  for Data Mining Applications (Week3-Week4)
• Data Warehouses Gathering Raw Data from
  Relational Databases and transforming into
  Information.
• Information Extraction and Data Processing
  Techniques
• Data Marts The need for building highly
  specialized data storages for data mining
  applications

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Course Syllabus

• Case Study 1 Working and experiencing on the
  properties of The Retail Banking Data Mart (Week
  4 Assignment1)
• Data Analysis Techniques (Week 5)
• Statistical Background
• Trends/ Outliers/Normalizations
• Principal Component Analysis
• Discretization Techniques
• Case Study 2 Working and experiencing on the
  properties of discretization infrastructure of
  The Retail Banking Data Mart (Week 5 Assignment
  2)
• Lecture Talk In-class discussion

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Course Syllabus

• Clustering Techniques (Week 6)
• K-Means Clustering
• Condorcet Clustering
• Other Clustering Techniques
• Case Study 3 Working and experiencing on the
  properties of the clustering infrastructure for
  The Retail Banking (Week 6 Assignment3)
• Lecture Talk In-class Discussion

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Course Syllabus

• Classification Techniques (Week 7- Week 8- Week
  9)
• Inductive Learning
• Decision Tree Learning
• Association Rules
• Regression
• Probabilistic Reasoning
• Bayesian Learning
• Case Study 4 Working and experiencing on the
  properties of the classification infrastructure
  of Propensity Score Card System for The Retail
  Banking (Assignment 4) Week 9

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Course Syllabus

• Prediction Techniques (Week 10- Week 11)
• Neural Networks
• Radial Basis Networks
• Reinforcement Learning
• Case Study 5 Working and experiencing on the
  properties of the prediction infrastructure of
  Propensity Score Card System for The Retail
  Banking (Assignment 5) (Week 11)
• Other Classification and Prediction Techniques
  (Week 12- Week 13)
• Text Mining and Web Mining
• Explanation Based Learning
• Rule Based Learning
• Genetic Algorithms
• Recurrent Networks
• Case Study 6 Working and experiencing on the
  properties of Genetic Algorithms infrastructure
  for Neural Network Topology Estimation
  (Assignment 6) (Week 13)

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Course Syllabus

• Assesment
• One midterm examination (35)
• One final examination (55)
• In-class reviewed Case Studies Based Assignments
  (10)
• There will be six assignments for each reviewed
  case studies. The assignments encouraged to be
  done by groups of two or three people

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Course Syllabus

• Text Book
• Jiawei Han and Micheline Kamber, Data Mining
  Concepts and Techniques, 2nd ed., Morgan
  Kaufmann, 2006.
• Supplementary Books
• Hastie, R. Tibshirani, and J. Friedman, The
  Elements of Statistical Learning Data Mining,
  Inference, and Prediction, Springer-Verlag, 2001
• P.-N.Tan, M. Steinbach, and V. Kumar,
  Introduction to Data Mining, Addison-Wesley,
  2006. ISBN 0-321-32136-7
• Tom M. Mitchell, Machine Learning, McGraw-Hill,
  1997.
• C. M. Bishop, Pattern Recognition and Machine
  Learning, Springer 2007
• R. O. Duda, P. E. Hart, and D. G. Stork, Pattern
  Classification, 2ed., Wiley-Inter-science, 2001.

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Week1- What Is Data Mining?

• "Drowning in Data yet Starving for Knowledge"
• ???
• "Computers have promised us a
  fountain of wisdom but delivered a flood of data"
  William J. Frawley, Gregory
  Piatetsky-Shapiro, and Christopher J. Matheus

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Week1-What Is Data Mining?

• Data flood
• Information society produces vast amounts of data
• Data are generated by
• Bank, telecom, other business transactions ...
• Scientific data astronomy, biology, etc
• Web, text, image, and e-commerce

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Week1-What Is Data Mining?

• ATT handles billions of calls per day
• As of 2003, according to Winter Corp. Survey,
• ATT has a 26 TB decision-support database.
• Web
• 1998 26 million pages
• 2003 Google searches 4 billion pages, many
  hundreds TB
• 2005 Google searches 8 billion pages
• 2008 1 trillion (1,000,000,000,000) pages.

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Week1-What Is Data Mining?

• UC Berkeley 2003 estimate
• 5 exabytes (5 million terabytes) of new data was
  created in 2002.
• Twice as much information was created in 2002 as
  in1999 (growth rate about 30 a year)
• Other growth rate estimates are even higher
• Very few data will ever be looked at by a human
• Tools are needed to make sense and use of data

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Week1-What Is Data Mining?

• Data
• raw
• atomic
• Information
• processed
• re-organized
• grouped
• Knowledge
• patterns, models, findings behind Information
• Wisdom
• perfect orchestration of Knowledge

Data (Operation)
Information (Analytic)
Data
Knowledge
Wisdom
Where is the wisdom we have lost in knowledge?
Where is the knowledge we have lost in
information?
T. S. Eliot
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Week1-What Is Data Mining?

• Hypothesis
• current data bases contain a lot of potentially
  important knowledge that can be used for
  wise-decisionining
• Mission of DM
• find it !!!

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Week1-What Is Data Mining?

• Data Mining (Alternative Name Knowledge
  Discovery in Databases KDD) definitions
• mining knowledge from data
• process of extracting interesting (non-trivial,
  implicit, previously unknown and potentially
  useful) knowledge or patterns from data in large
  databases.
• discover knowledge that characterizes general
  properties of data
• discover patterns on the previous and current
  data in order to make predictions on future data

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Week1-What Is Not Data Mining?

• "Torturing data until it confesses ... and if you
  torture it enough, it will confess to anything"
  Jeff Jonas, IBM
• "An Unethical Econometric practice of massaging
  and manipulating the data to obtain the desired
  results" W.S. Brown Introducing Econometrics
• "A buzz word for what used to be known as DBMS
  reports" An Anonymous Data Mining Skeptic

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Week1-What Is Data Mining?
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Week1-What Is Data Mining?

• Data Mining -an interdisciplinary field
• Databases
• Statistics
• High Performance Computing
• Machine Learning
• Visualization
• Mathematics

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Week1-What Is Data Mining?

• Data Mining -an interdisciplinary field
• Large Data sets in Data Mining
• Efficiency of Algorithms is important
• Scalability of Algorithms is important
• Real World Data
• Lots of Missing Values
• Pre-existing data - not synthetic
• Data not static - prone to updates
• Domain Knowledge in the form of integrity
  constraints available.
• Exploratory data analysis

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Week1-Data Mining Application Examples

• Credit Assessment
• Stock Market Prediction
• Fault Diagnosis in Production Systems
• Medical Discovery
• Fraud Detection
• Hazard Forecasting
• Buying Trends Analysis
• Organizational Restructuring
• Target Mailing
• ---

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Week1-Data Mining Application Examples

• Credit Assessment
• Stock Market Prediction
• Fault Diagnosis in Production Systems
• Medical Discovery
• Fraud Detection
• Hazard Forecasting
• Buying Trends Analysis
• Organizational Restructuring
• Target Mailing
• ---

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Week1-Data Mining Application Examples

• Can I develop a general characterization/profile
  of different investor types? (characterization)
• What characteristics distinguish between Online
  and Broker investors? (classification)
• Can I develop a model which will predict the
  average trades/month for a new investor?
  (regression)

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Week1-Data Mining Application Examples

• the natural question is to predict the Diagnosis
  from the symptoms (Medical Diagnosis Prediction)

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Week1-Data Mining Application Examples

• Assessing Credit Risk
• Situation Person applies for a loan
• Task Should a bank approve the loan?
• Need to predict the credit risk of the person
  people with bad credit are not likely to repay.

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Week1-Data Mining Application Examples

• 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 Advances in Knowledge
• Discovery and Data Mining, also bought Data
• Mining Practical Machine Learning Tools and
• Techniques with Java Implementations
• Recommendation program is quite successful

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Week 1-End

• read
• Course Text Book Chapter 1