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Clustering and Association Modeling Using IBM SPSS Modeler (v18) (0A047G)

Detailed Course Outline

1: Introduction to clustering and association models • 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

 

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