A reduction algorithm for approximating a (nonmetric) dissimilarity by a tree distance

We propose a development stemming from Roux (1988). The principle is progressively to modify the dissimilarities so that every quadruple satisfies not only the additive inequality, as in Roux's method, but also all triangle inequalities. Our method thus ensures that the results are tree distances even when the observed dissimilarities are nonmetric. The method relies on the analytic solution of the least-squares projection onto a tree distance of the dissimilarities attached to a single quadruple. This goal is achieved by using geometric reasoning which also enables an easy proof of algorithm's convergence. This proof is simpler and more complete than that of Roux (1988) and applies to other similar reduction methods based on local least-squares projection. The method is illustrated using Case's (1978) data. Finally, we provide a comparative study with simulated data and show that our method compares favorably with that of Studier and Keppler (1988) which follows in the ADDTREE tradition (Sattath and Tversky 1977). Moreover, this study seems to indicate that our method's results are generally close to the global optimum according to variance accounted for.We offer sincere thanks to Gilles Caraux, Bernard Fichet, Alain Guénoche, and Maurice Roux for helpful discussions, advice, and for reading the preliminary versions of this paper. We are grateful to three anonymous referees and to the editor for many insightful comments. This research was supported in part by the GREG and the IA² network.