Tree Models of Similarity and Association: Quantitative Applications in the Social Sciences, cartea 112
Autor James E. Corteren Limba Engleză Electronic book text – 29 iun 1996
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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
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>
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.
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.