title: Modelling dissimilarity: generalizing ultrametric and additive tree representations.
abstract: Methods for the hierarchical clustering of an object set produce a sequence of nested partitions such that object classes within each successive partition are constructed from the union of object classes present at the previous level. Any such sequence of nested partitions can in turn be characterized by an ultrametric. An approach to generalizing an (ultrametric) representation is proposed in which the nested character of the partition sequence is relaxed and replaced by the weaker requirement that the classes within each partition contain objects consecutive with respect to a fixed ordering of the objects. A method for fitting such a structure to a given proximity matrix is discussed, along with several alternative strategies for graphical representation. Using this same ultrametric extension, additive tree representations can also be generalized by replacing the ultrametric component in the decomposition of an additive tree (into an ultrametric and a centroid metric). A common numerical illustration is developed and maintained throughout the paper.
authors.affiliation: Department of Psychology, University of Illinois, 603 East Daniel Street, Champaign, IL 61820, USA.
authors.forename: L
authors.initials: L
authors.lastname: Hubert
authors.forename: P
authors.initials: P
authors.lastname: Arabie
authors.forename: J
authors.initials: J
authors.lastname: Meulman
mesh_terms: D016000:Cluster Analysis; D003267:Contraception / Q000145:classification / Q000706:statistics & numerical data; D003663:Decision Trees; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D011594:Psychometrics / Q000379:methods*