A least squares algorithm for fitting additive trees to proximity data
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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 wordstree structures clustering proximity data
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