id: 03938326
dt: j
an: 03938326
au: de Soete, Geert; DeSarbo, Wayne S.; Carroll, J.Douglas
ti: Optimal variable weighting for hierarchical clustering: An alternating
    least-squares algorithm.
so: J. Classif. 2, 173-192 (1985).
py: 1985
pu: Springer-Verlag, New York
la: EN
cc: 
ut: mathematical programming; variable importance; new methodology; ultrametric
    tree; profile data; Euclidean distances; classification; alternating
    least-squares algorithm; ethnic group rating data; additive, multiple,
    and three-way trees
ci: 
li: doi:10.1007/BF01908074
ab: Summary: This paper presents the development of a new methodology which
    simultaneously estimates in a least-squares fashion both an ultrametric
    tree and respective variable weightings for profile data that have been
    converted into (weighted) Euclidean distances. We first review the
    relevant classification literature on this topic. The new methodology
    is presented including the alternating least-squares algorithm used to
    estimate the parameters. The method is applied to a synthetic data set
    with known structure as a test of its operation. An application of this
    new methodology to ethnic group rating data is also discussed. Finally,
    extensions of the procedure to model additive, multiple, and three-way
    trees are mentioned.
rv: