title: Merging of distance matrices and classification by dynamic clustering.
abstract: The graphical representation of distance matrices in a Euclidean space allows the merging of two distance matrices since the two matrices have shared elements. The graphical representation of the merging of the two distance matrices is associated with a robust method of classification that allows one to distinguish species for which membership to a cluster cannot be established with certainty. These possibilities are exploited to test the consistency of phylogenetic trees, and to establish exact relations between species for which one possesses different independent distance measurements (distance matrices established from several types of sequences for instance). The whole set of programs is written in BASIC and runs on microcomputers.
authors.affiliation: Centre de Génétique Moleculaire du CNRS, Université Pierre et Marie Curie, Paris VI, Gif sur Yvette, France.
authors.forename: M O
authors.initials: MO
authors.lastname: Delorme
authors.forename: A
authors.initials: A
authors.lastname: Hénaut
mesh_terms: D002965:Classification; D008432:Mathematical Computing; D009716:Numerical Analysis, Computer-Assisted; D010802:Phylogeny; D012107:Research Design; D012984:Software