After a general introduction to multivariate statistical analyses, we focus on describing the task of multivariate classification, distinguishing its non-hierarchical and hierarchical forms. Focusing on hierarchical agglomerative classification methods (cluster analysis), we highlight the important decisions that must be made regarding the measurement of dissimilarity (distance) among objects. Following this, we explain the construction of dendrograms representing this hierarchical classification. We also briefly mention divisive classification methods, focusing on the TWINSPAN method. The methods described in this chapter are accompanied by a carefully-explained guide to the R code needed for their use, in this case employing the cluster package.
Review the options below to login to check your access.
Log in with your Cambridge Aspire website account to check access.
If you believe you should have access to this content, please contact your institutional librarian or consult our FAQ page for further information about accessing our content.