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

Course content updated by LearnQuest
Price
386 - 1,130 SGD
Duration
8 Hours
Rating
Course
0E048GSCN
Available Formats
Self-Paced Training
IBM Business Partner
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  • Learn on Demand
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 386 SGD
  • Date: 23-Aug-2021
    Time: 9AM - 5PM Beijing Time
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 1,130 SGD
  • Date: 20-Sep-2021
    Time: 9AM - 5PM Beijing Time
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 1,130 SGD
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Course Description

Overview

Contains: PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.

Clustering and Association Modeling Using IBM SPSS Modeler (v18.1) introduces modelers to two specific classes of modeling that are available in IBM SPSS Modeler: clustering and associations. Participants will explore various clustering techniques that are often employed in market segmentation studies. Participants will also explore how to create association models to find rules describing the relationships among a set of items, and how to create sequence models to find rules describing the relationships over time among a set of items.

If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course. http://www.ibm.com/training/terms

Objectives

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

Audience

Modelers, Analysts

Prerequisites

    • 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)

Topics

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

 

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Self-Paced Training Info

Learn at your own pace with anytime, anywhere training

  • Same in-demand topics as instructor-led public and private classes.
  • Standalone learning or supplemental reinforcement.
  • IBM SPVC content varies by course but at a minimum always contains a copy of the course materials and 30 day access to labs.
  • View the IBM Self-Paced version of this outline and what is included in the SPVC course.
  • Learn more about e-Learning

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Self-Paced Training Terms & Conditions

THIS IS A SELF-PACED VIRTUAL CLASS. AFTER YOU REGISTER, YOU HAVE 30 DAYS TO COMPLETE THE COURSE.

This is a Self-Paced virtual class; it is intended for students who do not need the support of a classroom instructor. If you feel you would better benefit from having access to a Subject Matter Expert, please enroll in the Instructor-Led version instead. Minimal technical support is provided to address issues with accessing the platform or problems within the lab environment.

Before you enroll, review the system requirements to ensure that your system meets the minimum requirements for this course. AFTER YOU ARE ENROLLED IN THIS COURSE, YOU WILL NOT BE ABLE TO CANCEL YOUR ENROLLMENT. You are billed for the course when you submit the enrollment form. Self-Paced Virtual Classes are non-refundable. Once you purchase a Self-Paced Virtual Class, you will be charged the full price.

After you receive confirmation that you are enrolled, you will be sent further instructions to access your course material and remote labs. A confirmation email will contain your online link, your ID and password, and additional instructions for starting the course.

You can start the course at any time within 12 months of enrolling for the course. After you register/start the course, you have 30 days to complete your course. Within this 30 days, the self-paced format gives you the opportunity to complete the course at your convenience, at any location, and at your own pace. The course is available 24 hours a day.

If the course requires a remote lab system, the lab system access is allocated on a first-come, first-served basis. When you are not using the elab system, ensure that you suspend your elab to maximize your hours available to use the elab system.

Click the Skytap Connectivity Test button to ensure this computer's hardware, software and internet connection works with the SPVC Lab Environment.

Click the Skytap Connectivity Documentation button to read about the hardware, software and internet connection requirements.

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