Data Mining Techniques: Theory and Practice

1. Introduction to Data Mining

• What is data mining?
• Directed and undirected data mining
• Models
• Profiling and prediction

2. Data Mining Methodology

• Why have a methodology?
• How data miners can inadvertently learn things that are not true
• Translating business problems into data mining problems
• The importance of model stability
• Finding the right input variables
• Sampling to create balanced model sets
• Partitioning to create training, validation, and test sets
• Data preparation
• Model assessment

3. Data Exploration

• Developing intuition about data
• Data structure
• Data types
• Data values
• Exploring distributions
• Summary statistics
• Histograms
• using SAS Enterprise Miner for data exploration

4. Regression Models

• The null hypothesis
• Statistical significance
• Confidence bounds
• Variance and standard deviation
• Standardized values
• Correlation
• Linear regression
• Logistic regression
• Using SAS Enterprise Miner to build regression models

5. Decision Trees

• Decision trees as data exploration and classification tools
• Decision trees for modeling and scoring
• Decision trees for variable selection
• Alternate representations of decision trees
• Algorithms used to build decision trees
• Splitting criteria
• Recognizing instability and overfitting in decision tree models
• Capturing interactions between variables
• Using SAS Enterprise Miner to build decision trees

6. Neural Networks

• Origins of neural networks
• Neural networks compared with regression
• Algorithms used to train neural networks
• Data preparation requirements for neural networks
• Picking appropriate inputs for neural networks
• Creating neural network models using SAS Enterprise Miner

7. Memory-Based Reasoning

• Similarity and distance
• Distance metrics appropriate for different kinds of data
• The role of the training set in memory-based reasoning (MBR)
• Combining the votes of several neighbors
• Other K-nearest neighbor techniques
• Collaborative filtering
• Using the SAS Enterprise Miner MBR node

8. Clustering

• More on similarity and distance
• The k-means algorithm
• Divisive clustering
• Agglomerative clustering
• Data preparation for clustering
• Interpreting clusters
• Finding clusters with SAS Enterprise Miner

9. Survival Analysis

• Origins of survival analysis
• How business data is different from clinical data
• Hazards and hazard charts
• Retention curves and survival curves
• Calculating survival from retention
• Calculating hazards empirically
• Parametric hazard models
• Censoring
• Competing risks
• Survival-based forecasting
• Using SAS code in SAS Enterprise Miner to create survival curves

10. Association Rules

• Market basket analysis
• Association rules
• Sequential pattern analysis
• Using SAS Enterprise Miner to discover associations in retail data

11. Link Analysis

• Background on graph theory
• Sphere of influence
• Using link analysis to generate derived variables
• Graph-coloring algorithm
• Kleinberg's algorithm

12. Genetic Algorithms

• Optimization techniques and problems (SAS/OR software)
• Other algorithms
• Linear programming problems
• Genetic algorithms

Exercises or hands-on workshops are included with most SAS courses.

• Business analysts and their managers
• Statisticians