1. Introduction to Data Mining
Informatika
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2. Outline
 Motivation: Why Data Mining?
 What is Data Mining?
 Data Mining Applications
 Issues in Data Mining
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3. Data vs. Information
 Society produces massive amounts of data
 business, science, medicine, economics, sports, …
 Potentially valuable resource
 Raw data is useless
 need techniques to automatically extract information
 Data: recorded facts
 Information: patterns underlying the data
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4. Multidisciplinary Field
Database
Statistics
Technology
Machine
Learning
Data Mining Visualization
Artificial Intelligence Other
(Machine Learning – Neural Network) Disciplines
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5. Terminology
 Gold Mining
 Knowledge mining from databases
 Knowledge extraction
 Data/pattern analysis
 Knowledge Discovery Databases or KDD
 Information harvesting
 Business intelligence
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6. KDD Process
Database
Selection Data Training Data Model,
Transformation Preparation Data Mining Patterns
Evaluation,
Verification
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7. Data Mining Tasks
 Exploratory Data Analysis
 Predictive Modeling: Classification and Regression
 Descriptive Modeling
 Cluster analysis/segmentation
 Discovering Patterns and Rules
 Association/Dependency rules
 Sequential patterns
 Temporal sequences
 Deviation detection
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8. Data Mining Tasks
 Concept/Class description: Characterization
and discrimination
 Generalize, summarize, and contrast data
characteristics, e.g., dry vs. wet regions
 Association (correlation and causality)
 Multi-dimensional or single-dimensional association
age(X, “20-29”) ^ income(X, “60-90K”)  buys(X, “TV”)
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9. Data Mining Tasks
 Classification and Prediction
 Finding models (functions) that describe and
distinguish classes or concepts for future prediction
 Example: classify countries based on climate, or
classify cars based on gas mileage
 Presentation:
 If-THENrules, decision-tree, classification rule,
neural network
 Prediction: Predict some unknown or missing
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10. Data Mining Tasks
 Cluster analysis
 Class label is unknown: Group data to form
new classes,
 Example: cluster houses to find distribution
patterns
 Clustering based on the principle: maximizing
the intra-class similarity and minimizing the
interclass similarity
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11. Data Mining Applications
 Science: Chemistry, Physics, Medicine
 Biochemical analysis
 Remote sensors on a satellite
 Telescopes – star galaxy classification
 Medical Image analysis
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12. Data Mining Applications
 Bioscience
 Sequence-based analysis
 Protein structure and function prediction
 Protein family classification
 Microarray gene expression
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13. Data Mining Applications
 Pharmaceutical companies, Insurance
and Health care, Medicine
 Drug development
 Identify successful medical therapies
 Claims analysis, fraudulent behavior
 Medical diagnostic tools
 Predict office visits
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14. Data Mining Applications
 Financial Industry, Banks, Businesses, E-
commerce
 Stock and investment analysis
 Identify loyal customers vs. risky customer
 Predict customer spending
 Risk management
 Sales forecasting
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15. Data Mining Applications
 Retail and Marketing
 Customer buying patterns/demographic
characteristics
 Mailing campaigns
 Market basket analysis
 Trend analysis
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16. Data Mining Applications
 Database analysis and decision support
 Market analysis and management
 target marketing, customer relation management, market
basket analysis, cross selling, market segmentation
 Risk analysis and management
 Forecasting, customer retention, improved underwriting,
quality control, competitive analysis
 Fraud detection and management
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17. Data Mining Applications
 Sports and Entertainment
 IBM Advanced Scout analyzed NBA game
statistics (shots blocked, assists, and fouls) to
gain competitive advantage for New York
Knicks and Miami Heat
 Astronomy
 JPL and the Palomar Observatory discovered
22 quasars with the help of data mining
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18. DATA MINING EXAMPLES
 Grocery store
 NBA
 Banking and Credit Card scoring
 Fraud detection
 Personalization & Customer Profiling
 Campaign Management and Database
Marketing
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19. Data Mining Challenges
 Computationally expensive to investigate all
possibilities
 Dealing with noise/missing information and
errors in data
 Choosing appropriate attributes/input
representation
 Finding the minimal attribute space
 Finding adequate evaluation function(s)
 Extracting meaningful information

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20. Summary
 Data mining: discovering interesting patterns
from large amounts of data
 A KDD process includes data cleaning, data
integration, data selection, transformation, data
mining, pattern evaluation, and knowledge
presentation
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21. Summary
 Mining can be performed in a variety of
information repositories
 Data mining functionalities: characterization,
association, classification, clustering, outlier
and trend analysis, etc.
 Classification of data mining systems
 Major issues in data mining
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22. Kinds of Data Mining
 Decision Tree Learning
 Clustering
 Neural Networks
 Association Rules
 Support Vector Machines
 Genetic Algorithms
 Nearest Neighbor Method
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23. DECISION TREE FOR THE CONCEPT
“Play Tennis”
Day Outlook Temp Humidity Wind PlayTennis
D1 Sunny Hot High Weak No
D2 Sunny Hot High Strong No
D3 Overcast Hot High Weak Yes
D4 Rain Mild High Weak Yes
D5 Rain Cool Normal Weak Yes
D6 Rain Cool Normal Strong No
D7 Overcast Cool Normal Strong Yes
D8 Sunny Mild High Weak No
D9 Sunny Cool Normal Weak Yes
D10 Rain Mild Normal Weak Yes
D11 Sunny Mild Normal Strong Yes
D12 Overcast Mild High Strong Yes
D13 Overcast Hot Normal Weak Yes
D14
Mitchell, 1997 Rain Mild High Strong No
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24. DECISION TREE FOR THE CONCEPT
“Play Tennis”
[Mitchell,1997]
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