A least squares algorithm for fitting additive trees to proximity data Authors
Notes And Comments
Received: 18 December 1981 Revised: 13 May 1983 DOI:
Cite this article as: De Soete, G. Psychometrika (1983) 48: 621. doi:10.1007/BF02293884 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.
Key words tree structures clustering proximity data
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.
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© The Psychometric Society 1983