IBM 0A048G - Clustering and Association Modeling Using IBM SPSS Modeler (v18.1.1)
Startdaten und Startorte
placeim Büro, Homeoffice, Meetingraum 4. Jul 2024Details ansehen event 4. Juli 2024, 09:00-17:00, im Büro, Homeoffice, Meetingraum, Seminar 30007 |
Beschreibung
Course Outline
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 Kohon…
Frequently asked questions
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Course Outline
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
Objective
- 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)
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