Library

X
feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Electronic Resource
    Electronic Resource
    Optimal variable weighting for hierarchical clustering: An alternating least-squares algorithm (1985)
    Springer
    Journal of classification 2 (1985), S. 173-192 
    Details
    ISSN: 1432-1343
    Keywords: Ultrametric trees ; Mathematical programming ; Variable importance
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract 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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01908074
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 2
    Electronic Resource
    Electronic Resource
    Least squares algorithms for constructing constrained ultrametric and additive tree representations of symmetric proximity data (1987)
    Springer
    Journal of classification 4 (1987), S. 155-173 
    Details
    ISSN: 1432-1343
    Keywords: Hierarchical clustering ; Path length trees ; Mathematical programming ; Constrained classification methods
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract A mathematical programming algorithm is developed for fitting ultrametric or additive trees to proximity data where external constraints are imposed on the topology of the tree. The two procedures minimize a least squares loss function. The method is illustrated on both synthetic and real data. A constrained ultrametric tree analysis was performed on similarities between 32 subjects based on preferences for ten odors, while a constrained additive tree analysis was carried out on some proximity data between kinship terms. Finally, some extensions of the methodology to other tree fitting procedures are mentioned.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01896984
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...