Abstract
In a cluster analysis of a multivariate data set, it may happen that one or two observations have a disproportionately large effect on the analysis, in the sense that their removal causes a dramatic change to the results. It is important to be able to identify such influential observations, and the present paper addresses this problem. To do so, we must first quantify the effect of a single observation. Various definitions are discussed, and criteria for identifying influential observations are investigated; the minimum spanning tree and the number of neighbours of each observation are considered. The investigation concentrates on single-link cluster analysis, although complete-link analysis is also briefly discussed. Patterns emerge in both real and simulated data, which suggest ways of predicting observations with no effect and those with the greatest effect. It is not necessary to recalculate the results with each observation omitted—an economy of presentation as well as labour.
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.
