Tree Models of Similarity and Association

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.

Table des matières

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

A propos de l'auteur

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.

Détails du produit

• Auteurs: James E. Corter, Corter James E.
• Edition: O'Reilly
• Langues: Anglais
• Format d'édition: Livre de poche
• Sortie: 31.12.2020
• EAN: 9780803957077
• ISBN: 978-0-8039-5707-7
• Pages: 72
• Poids: 110 g
• Thèmes: Quantitative Applications in the Social Sciences; Quantitative Applications in t; Quantitative Applications in the Social Sciences; Quantitative Applications in t
• Catégories: Sciences naturelles, médecine, informatique, technique; Sciences naturelles, médecine, informatique, technique > Médecine > Général; Sciences sociales, droit, économie > Sociologie > Théories sociologiques; Social Theory, MATHEMATICS / Applied, SOCIAL SCIENCE / Sociology / Social Theory, Applied mathematics