Detailed Course Outline
1: Introduction to clustering and association modeling
- • Identify the association and clustering modeling techniques available in IBM SPSS Modeler
- • Explore the association and clustering modeling techniques available in IBM SPSS Modeler
- • Discuss when to use a particular technique on what type of data
2: Clustering models and K-Means clustering
- • Identify basic clustering models in IBM SPSS Modeler
- • Identify the basic characteristics of cluster analysis
- • Recognize cluster validation techniques
- • Understand K-Means clustering principles
- • Identify the configuration of the K-means node
3: Clustering using the Kohonen network
- • Identify the basic characteristics of the Kohonen network
- • Understand how to configure a Kohonen node
- • Model a Kohonen network
4: Clustering using TwoStep clustering
- • Identify the basic characteristics of TwoStep clustering
- • Identify the basic characteristics of Two Step AS clustering
- • Model and analyze a TwoStep clustering solution
5: Use Apriori to generate association rules
- • Identify three methods of generating association rules
- • Use the Apriori node to build a set of association rules
- • Interpret association rules
6: Use advanced options in Apriori
- • Identify association modeling terms and rules
- • Identify evaluation measures used in association modeling
- • Identify the capabilities of the Association Rules node
- • Model associations and generate rules using Apriori
7: Sequence detection
- • Explore sequence detection association models
- • Identify sequence detection methods
- • Examine the Sequence node
- • Interpret the sequence rules and add sequence predictions to steams
8: Advanced Sequence detection
- • Identify advanced sequence detection options used with the Sequence node
- • Perform in-depth sequence analysis
- • Identify the expert options in the Sequence node
- • Search for sequences in Web log data
A: Examine learning rate in Kohonen networks (Optional
- • Understand how a Kohonen neural network learns
B: Association using the Carma model (Optional)
- • Review association rules
- • Identify the Carma model
- • Identify the Carma node
- • Model associations and generate rules using Carma