1. Data Mining
Lecture 1

2. Instructor Info Name: Ertan Karakurt
Contact :
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
...

3. Instructor Info Ertan Karakurt founder of İzmir based Akıllı Sistemler
fuzzy/exact searching/matching engine for Databases:
search space analyzing, learning
algorithm space analyzing, learning
parallelization architecture

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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)

10. 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

11. Course Syllabus Text Book: Supplementary Books:
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, ISBN:
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.

12. 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

13. 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

14. Week1-What Is Data Mining?
AT&T handles billions of calls per day
As of 2003, according to Winter Corp. Survey,
AT&T 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.

15. 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

16. Week1-What Is Data Mining?
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

17. 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 !!!

18. 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

19. 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

20. Week1-What Is Data Mining?

21. Week1-What Is Data Mining?
Data Mining -an interdisciplinary field
Databases
Statistics
High Performance Computing
Machine Learning
Visualization
Mathematics

22. 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

23. 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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24. 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
---

25. 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)

26. Week1-Data Mining Application Examples
the natural question is to predict the Diagnosis from the symptoms (Medical Diagnosis Prediction)

27. 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.

28. 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

29. Week 1-End
read
Course Text Book Chapter 1