论文信息 - Ultrametric tree representations of incomplete dissimilarity data

Ultrametric tree representations of incomplete dissimilarity data

The least squares algorithm for fitting ultrametric trees to proximity data originally proposed by Carroll and Pruzansky and further elaborated by De Soete is extended to handle missing data. A Monte Carlo evaluation reveals that the algorithm is capable of recovering an ultrametric tree underlying an incomplete set of error-perturbed dissimilarities quite well.

G. Soete

[1] J. Hartigan. REPRESENTATION OF SIMILARITY MATRICES BY TREES , 1967 .

[2] S. C. Johnson. Hierarchical clustering schemes , 1967, Psychometrika.

[3] C. J. Jardine,et al. The structure and construction of taxonomic hierarchies , 1967 .

[4] John A. Hartigan,et al. Clustering Algorithms , 1975 .

[5] M. J. D. Powell,et al. Restart procedures for the conjugate gradient method , 1977, Math. Program..

[6] P. Arabie,et al. Mapclus: A mathematical programming approach to fitting the adclus model , 1980 .

[7] G. Soete. A least squares algorithm for fitting additive trees to proximity data , 1983 .

[8] P. Arabie,et al. Indclus: An individual differences generalization of the adclus model and the mapclus algorithm , 1983 .

[9] W. DeSarbo,et al. The representation of three-way proximity data by single and multiple tree structure models , 1984 .

[10] Geert De Soete,et al. A least squares algorithm for fitting an ultrametric tree to a dissimilarity matrix , 1984, Pattern Recognit. Lett..

[11] George W. Furnas,et al. The estimation of ultrametric and path length trees from rectangular proximity data , 1984 .