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

Description

At Sixe Engineering we have been providing official IBM training around the world for over 12 years. Get the best training from our specialists in Europe. We have important discounts and offers for two or more students.

Course details

Target audience

Modelers, Analysts

Desired 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)

Instructors

The great majority of the IBM courses we offer are taught directly by our engineers. This is the only way we can guarantee the highest quality. We complement all the training with our own materials and laboratories, based on our experience during the deployments, migrations and courses that we have carried out during all these years.

Added value

Our courses are deeply role oriented. To give an example, the needs for technology mastery are different for developer teams and for the people in charge of deploying and managing the underlying infrastructure. The level of previous experience is also important and we take it very seriously. That is why beyond (boring) commands and tasks, we focus on solving the problems that arise in the day to day of each team. Providing them with the knowledge, competencies and skills required for each project. In addition, our documentation is based on the latest version of each product.

Agenda and course syllabus

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

Do you need to adapt this syllabus to your needs? Are you interested in other courses?  Ask us without obligation.

Locations for on-site delivery

• Austria: Vienna
• Belgium: Brussels, Ghent
• Denmark: Cophenhagen
• Estonia: Tallinn
• Finland: Helsinki
• France: Paris, Marseille, Lyon
• Germany: Berlin, Munich, Cologne, Hamburg
• Greece: Athens, Thessaloniki
• Italy: Rome
• Louxemburg: Louxembourg (city)
• Netherlands: Amsterdam
• Norway: Oslo
• Portugal: Lisbon, Braga, Porto, Coimbra
• Slovakia: Bratislava
• Slovenia: Bratislava
• Spain: Madrid, Sevilla, Valencia, Barcelona, Bilbao, Málaga
• Sweden: Stockholm
• Turkey: Ankara
• United Kingdom: London