Data Mining 1.1-1.5

Data Mining 1.1-1.5

1. 1.1

   • Wealth of info. and data
     • Growing gap of generation/understanding
     • world wide web
   • Make sense of data
     • Finding Patterns
       • making predictions
       • new insights
       • competitive advantage
       • structural
         • contact lens example
   • Machine Learning
     • Change in behavior
       • improved performance
     • learning vs. training

2. 1.2

   • the weather problem
     • defined by set of rules
       • decision list
     • numeric-attibute problem
       • numbers instead of categories
   • contact lenses example
     • Decision tree
       • simplifies
   • Iris Example
     • Regression
   • Soybean Classification
     • domain knowledge
       • conditions of one another

3. 1.3

   • Web Mining
     • Rank of web pages
     • learning algorithms
       • Queries
         • Advertising
   • Machine learning
     • Loan Company example
   • Screening Images
     • Satellite technology
       • Scanning for oil spills
   • Forecasting
     • Use of Historical data
     • Speeding up the process
       • > human counterpart
   • Diagnosis
     • finding faults
     • Training examples
   • Marketing/Sales
     • customer data
       • churn
       • adds value
   • Other Applications
     • Biology
     • Customer Support
     • Chemistry

4. 1.4

   • Statistics
     • Hypothesis testing
     • Decision Trees

5. 1.5

   • Bias
     • language
     • search
     • overfitting-avoidance
   • Enumerating the Concept Space
   • Generalization