Clustering and Association Modeling Using IBM SPSS Modeler (v18.1.1)

Modelers, Analysts

• Introduction to clustering and association modeling 
• Clustering models and K-Means clustering 
• Clustering using the Kohonen network 
• Clustering using TwoStep clustering 
• Use Apriori to generate association rules 
• Use advanced options in Apriori 
• Sequence detection 
• Advanced Sequence detection 
• Examine learning rate in Kohonen networks (Optional) 
• Association using the Carma model (Optional) 

• Experience using IBM SPSS Modeler• A familiarity with the IBM SPSS Modeler environment: creating models, creating streams, reading in data files, and assessing data quality• A familiarity with handling missing data (including Type and Data Audit nodes), and basic data manipulation (including Derive and Select nodes)

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 TwoStep-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