Cluster Analysis in R

Q-Step is a £19.5 million programme designed to promote a step-change in quantitative social science training. Over a five-year period from 2013, fifteen universities across the UK are delivering specialist undergraduate programmes, including new courses, work placements and pathways to postgraduate study. Expertise and resources will be shared across the higher education sector through an accompanying support programme, which will also forge links with schools and employers.

Glasgow Q Step Centre

This course will provide an introduction to methods in clustering analysis using the statistical programming package, R

Dr. Nema DeanSchool of Mathematics and StatisticsCluster analysis is a set of exploratory unsupervised learning methods that seek to discover a priori unknown group structure in quantitative data. This short course will introduce the participants to the basic idea of cluster analysis and a detailed tutorial on 2 classical and widely-used methods of cluster analysis: hierarchical agglomerative clustering and k-means. Although there will be some technical details, equations will be kept to a minimum and the focus will be building an intuition about the methods. Examples will be demonstrated in R and there will be short lab sessions for the course members to try out the methods in R themselves. The goal is for participants to end the course with an understanding of clustering, an awareness of when it is appropriate to use and when not, an ability to run and interpret a cluster analysis in R, issues and caveats to looks out for, and links to further methods of interest.Please note that a working knowledge of the R statistical software language (at least the ability to open and manipulate data files, run summaries and plot data) will be assumed. This will not be a course introducing the basics of R.

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Cluster Analysis in R

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Cluster Analysis in R

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