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A Least Squares Algorithm for Fitting Additive Trees to Proximity Data

Published online by Cambridge University Press:  01 January 2025

Geert De Soete
Geert De Soete*
Affiliation:
University of Ghent, Belgium
*
Requests for reprints should be sent to Geert De Soete, Department of Psychology, University of Ghent, Henri Dunantlaan 2, B-9000 Ghent, Belgium.
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Abstract

A least squares algorithm for fitting additive trees to proximity data is described. The algorithm uses a penalty function to enforce the four point condition on the estimated path length distances. The algorithm is evaluated in a small Monte Carlo study. Finally, an illustrative application is presented.

Keywords

tree structuresclusteringproximity data

Information

Type
Notes And Comments
Information
Psychometrika , Volume 48 , Issue 4 , December 1983 , pp. 621 - 626
Copyright
Copyright © 1983 The Psychometric Society

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Footnotes

The author is “Aspirant” of the Belgian “Nationaal Fonds voor Wetenschappelijk Onderzoek”. The author is indebted to Professor J. Hoste for providing computer facilities at the Institute of Nuclear Sciences at Ghent.

References

References Notes

Carroll, J. D., & Pruzansky, S. Fitting of hierarchical tree structure (HTS) models, mixtures of HTS models, and hybrid models via mathematical programming and alternating least squares. Paper presented at the U.S.-Japan Seminar on Theory, Methods, and Applications of Multidimensional Scaling and Related Techniques, San Diego, August 1975.Google Scholar
Furnas, G. W. The construction of random, terminally labeled, binary trees. Unpublished paper, Bell Laboratories, Murray Hill, New Jersey, 1981.Google Scholar

References

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