Description

This module introduces modelers to two specific classes of modeling that are available in IBM SPSS Modeler: clustering and associations. Professionals will explore various clustering techniques which are often employed in market segmentation studies. Professionals will also explore the ways to create association models to find out the rules for explaining the relationships among a set of items, along with the ways to create sequence models for finding out the rules, describing the relationships over time among a set of items.

Radiant Techlearning offers the Clustering and Association Modeling Using IBM SPSS Modeler (v18.1.1) training program in Classroom & Virtual Instructor Led / Online mode.

 

Duration: 1day

 

Learning objectives

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

Pre-requisites

  • Knowledge and understanding on IBM SPSS Modeler
  • A familiarity with creating models, creating streams, reading in data files, and assessing data quality using the IBM SPSS Modeler environment
  • A familiarity in working with missing data (including Type and Data Audit nodes), as well as basic data manipulation (including Derive and Select nodes)

Audience for the course

  • Modelers
  • Analysts

Course content

Introduction to clustering and association modeling

  • Identification of 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

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

Clustering using the Kohonen network

  • Identify the basic characteristics of the Kohonen network
  • Understand how to configure a Kohonen node
  • Model a Kohonen network

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

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

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

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

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

Examine learning rate in Kohonen networks (Optional)

  • Understand how a Kohonen neural network learns

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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