Introduction to Cluster Analysis

Description:

Outline

The course covers basic ideas and concepts in cluster analysis including hierarchical and non-hierarchical clustering methods and their application. Throughout the day, participants will have guided practicals in R using ecological data from the UK Census of population on how to choose and transform variables for cluster analysis, how to perform cluster analysis using different clustering techniques, and how to examine cluster results and cluster validity.

Course objectives

• Basic ideas and concepts of hierarchical and non-hierarchical cluster analysis
• Selection of variables for cluster analysis
• Choice and application of an appropriate clustering technique in R
• Determine an optimal cluster solution
• Cluster interpretation
• Cluster validation

Participants will develop an understanding of clustering methods and procedures, carrying out analysis in R. By the end of the course, they will be able to carry out preliminary analysis to select and transform variables for cluster analysis, choose a clustering method, evaluate and choose cluster solutions, and interpret cluster results.

The course will enable participants to develop practical skills to implement clustering techniques to identify structures or groupings within data, to better understand social, economic and spatial dimensions of populations, and categorise data for further analysis, for example for statistical modelling or in-depth case study research.

Prerequisites

Participants should have an understanding of basic data analysis techniques including correlation and regression analysis. Familiarity with R is desired but not essential.

Cost:

£195/£140/£60

Website and registration:

https://www.cmi.manchester.ac.uk/study/short/introductory/cluster-analysis/

Region:

North West

Keywords:

Cluster analysis

Related publications and presentations:

Cluster analysis

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