This one-day course follows the Introduction to IBM SPSS Modeler and Data Mining course or the Advanced Data Preparation with IBM SPSS Modeler and is designed for anyone who wishes to become familiar with the full range of modeling techniques available in IBM SPSS Modeler to segment (cluster) data and to create models with association or sequence data. For people wishing to successfully build such models using IBM SPSS Modeler, this course is an essential part of the learning process.

Pre-requisites

  • General computer literacy
  • Experience using IBM SPSS Modeler, including familiarity with the IBM SPSS Modeler environment, creating streams, reading in data files, assessing data quality and handling missing data (including the Type and Data Audit nodes), basic data manipulation (including the Derive and Select nodes), and creation of models
  • Prior completion of the Introduction to IBM SPSS Modeler and Data

High-level Curriculum

Lesson 1: Introduction to Association and Cluster Modeling Techniques in Modeler

  • Introduction
  • Clustering
  • Association Rules
  • Sequence Detection
  • Which technique, When?

Lesson 2: Techniques for Clustering

  • Introduction
  • What to look for when clustering
  • K-Means Clustering
  • The K-Means Node
  • Exploring the cluster profiles
  • Clustering with a Kohonen Network
  • The Kohonen Node
  • TwoStep Clustering

Lesson 3: Association Rules

  • Introduction
  • The Apriori Node
  • Using the Associations

Lesson 4: Advanced Association Rules

  • Introduction
  • Association Rules
  • Apriori
  • Carma
  • Apriori Expert Options
  • Carma Expert Options
  • Using a Method and Expert Options
  • Missing Data with Association Rules

Lesson 5: Sequence Detection

  • Introduction
  • Data Organization for Sequence Detection
  • The Sequence Node
  • Exploring Sequence
  • Model Predictions

Lesson 6: Advanced Sequence Detection

  • Introduction
  • Sequence Node
  • Sequence Node Expert Options
  • Sequence Results