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Tree Models of Similarity and Association: Quantitative Applications in the Social Sciences, cartea 112

Autor James E. Corter
en Limba Engleză Electronic book text – 29 iun 1996
Clustering and tree models are being widely used in the social and biological sciences to analyze similarity relations. This volume describes how matrices of similarities or associations among entities can be modelled using trees, and explains some of the issues that arise in performing such analyses and interpreting the results correctly. James E Corter distinguishes ultrametric trees from additive trees and discusses how specific aspects of each type of tree can be interpreted through the use of applications as examples. He concludes with a discussion of when tree models might be preferable to spatial geometric models.
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Specificații

ISBN-13: 9781452215662
ISBN-10: 1452215669
Pagini: 72
Dimensiuni: 140 x 216 mm
Ediția:1
Editura: SAGE Publications
Colecția Sage Publications, Inc
Seria Quantitative Applications in the Social Sciences

Locul publicării:Thousand Oaks, United States

Cuprins

Introduction
Two Types of Tree Models
Algorithms for Fitting Trees to Data
Practical Issues and Applications
Some Extensions of Tree Models
Discussions and Conclusions
<i>Appendix A: Mathematical Programming</i>
<i>Appendix B: Availability of Software for Fitting Trees</i>
<i>Appendix C: Estimating Fit of a Tree Using Multiple Regression</i>

Descriere

Clustering and tree models are being widely used in the social and biological sciences to analyze similarity relations. This volume describes how matrices of similarities or associations among entities can be modelled using trees, and explains some of the issues that arise in performing such analyses and interpreting the results correctly. James E Corter distinguishes ultrametric trees from additive trees and discusses how specific aspects of each type of tree can be interpreted through the use of applications as examples. He concludes with a discussion of when tree models might be preferable to spatial geometric models.

Notă biografică

Scholarly Interests

Computational models of human learning and categorization. Judgment and decision-making. Clustering and scaling methods for multivariate data. Statistics expertise and probability problem-solving. Evaluation of educational technology innovations.