The RokSprocket Module needs the RokSprocket Component enabled.

A Primer on Segmentation Analysis

A Primer on Segmentation Analysis

Several clustering methods. Learn about their principle, conditions of use, data preparation phases, common pitfalls as well as good practices. Several real life applications are presented.

Course Details

Learning Objectives

Learning Objectives:

Upon completion of this module, participants will be able: 

  • To understand what cluster analysis is about
  • To master the general principles underlying cluster analysis
  • To understand the key issues to address in cluster analysis & common pitfalls
  • To know about the most commonly used modelling techniques in cluster analysis

Target Audience

Target Audience:

This applied training session is aimed at people who need to understand the steps involved in the clustering process.



This module introduces key concepts in cluster analysis. It assumes that participants either have no previous knowledge of statistics or that they have not used statistics for a long time.

Course Outline

Course Outline:
  • Goals & Context of Use of Cluster Analysis
  • Principle Underlying Cluster Analysis
    • Data Selection & Preparation
    • Clustering Method Selection: Principle, Advantages & Limitations
      • Hierarchical Methods
      • Optimisation Methods
      • Model-Based Methods
      • Other Methods
    • Assessing the Quality of the Clustering Solution
    • Use & Interpretation of Clusters
    • Summary

    Practical Info

    Practical Info:

    Recommended Course Duration: 1-1.5 day


    Related Sessions

    • Learn about preference mapping techniques to explore and understand consumer preferences. Applications dealing with segmentation and the identification of niche markets are discussed. Focus on pitfalls and good practices.

    • Conceptually similar to PCA, correspondence analysis a method is designed for discovering associstions in categorical rather than continuous data. Discover the informative 2D-plots for efficient data mapping.

    • Learn how to take data (consumers, genes, stores, ...) and organise them into homogeneous groups for use in many applications, such as market analysis and biomedical data analysis, or as a pre-processing step for many data mining tasks. Cluster analysis comprises a collection of powerful techniques. Learn about this very active field of research in statistics and data mining, and discover new techniques.

    • Once influential factors are identified, the next goal consists of optimising their settings. This module covers the construction of experimental designs for optimisation.  Data modeling is carried out with response surface methodology.