Abstract
We consider two dissimilarity measures between variables that take account of the variances of the variables as well as of their correlations. When variables are standardised, we retrieve widely used dissimilarity measures. The first dissimilarity measure is Euclidean distance and is suitable in studies where negative correlation between variables implies disagreement. The second dissimilarity measure is a Procrustean distance and is suitable in situations where both positive and negative correlations imply agreement. We also discuss aggregation strategies in order to carry out hierarchical clustering and find groups of variables. Applications in consumer and sensory studies are outlined.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
