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  • Cluster Analysis  (3)
  • Constrained classification methods  (1)
  • 1
    Electronic Resource
    Electronic Resource
    Simultaneous multidimensional unfolding and cluster analysis: An investigation of strategic groups (1991)
    Springer
    Marketing letters 2 (1991), S. 129-146 
    Details
    ISSN: 1573-059X
    Keywords: Marketing Strategy ; Multidimensional Scaling ; Cluster Analysis ; Strategic Groups
    Source: Springer Online Journal Archives 1860-2000
    Topics: Economics
    Notes: Abstract This paper develops a maximum likelihood based methodology for simultaneously performing multidimensional unfolding and cluster analysis on two-way dominance or profile data. This new procedure utilizes mixtures of multivariate conditional normal distributions to estimate a joint space of stimulus coordinates and K ideal points, one for each cluster or group, in a T-dimensional space. The conditional mixture, maximum likelihood methodology is introduced together with an E-M algorithm utilized for parameter estimation. A marketing strategy application is provided with an analysis of PIMS data for a set of firms drawn from the same competitive industry to determine strategic groups, while simultaneously depicting strategy-performance relationships.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00436033
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    A new clustering methodology for the analysis of sorted or categorized stimuli (1991)
    Springer
    Marketing letters 2 (1991), S. 267-279 
    Details
    ISSN: 1573-059X
    Keywords: Cluster Analysis ; Categorization ; Sorting Tasks ; Maximum Likelihood Estimation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Economics
    Notes: Abstract This paper introduces a new stochastic clustering methodology devised for the analysis of categorized or sorted data. The methodology reveals consumers' common category knowledge as well as individual differences in using this knowledge for classifying brands in a designated product class. A small study involving the categorization of 28 brands of U.S. automobiles is presented where the results of the proposed methodology are compared with those obtained from KMEANS clustering. Finally, directions for future research are discussed.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00554131
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    Paper (German National Licenses)
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  • 3
    Electronic Resource
    Electronic Resource
    A new clustering methodology for the analysis of sorted or categorized stimuli (1991)
    Springer
    Marketing letters 2 (1991), S. 267-279 
    Details
    ISSN: 1573-059X
    Keywords: Cluster Analysis ; Categorization ; Sorting Tasks ; Maximum Likelihood Estimation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Economics
    Notes: Abstract This paper introduces a new stochastic clustering methodology devised for the analysis of categorized or sorted data. The methodology reveals consumers' common category knowledge as well as individual differences in using this knowledge for classifying brands in a designated product class. A small study involving the categorization of 28 brands of U.S. automobiles is presented where the results of the proposed methodology are compared with those obtained from KMEANS clustering. Finally, directions for future research are discussed.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02404077
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    Paper (German National Licenses)
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  • 4
    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
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    Paper (German National Licenses)
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