Skip to content
Register Sign in Wishlist
The Structural Representation of Proximity Matrices with MATLAB

The Structural Representation of Proximity Matrices with MATLAB

$102.00 (P)

Part of ASA-SIAM Series on Statistics and Applied Probability

  • Date Published: March 2006
  • availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
  • format: Paperback
  • isbn: 9780898716078

$ 102.00 (P)
Paperback

This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
Unavailable Add to wishlist

Looking for an examination copy?

This title is not currently available for examination. However, if you are interested in the title for your course we can consider offering an examination copy. To register your interest please contact collegesales@cambridge.org providing details of the course you are teaching.

Description
Product filter button
Description
Contents
Resources
Courses
About the Authors
  • The Structural Representation of Proximity Matrices with MATLAB presents and demonstrates the use of functions within a MATLAB computational environment, affecting various structural representations for the proximity information that is assumed to be available on a set of objects. The representations included in the book have been developed primarily in the behavioral sciences and applied statistical literature, although interest in these topics now extends more widely to such fields as bioinformatics and chemometrics. This book is divided into three main sections, each based on the general class of representations being discussed. Part I develops linear and circular unidimensional and multidimensional scaling using the city-block metric as the major representational device. Part II discusses characterizations based on various graph-theoretic tree structures, specifically those referred to as ultrametrics and additive trees. Part III uses representations defined solely by order properties, particularly emphasizing what are called (strongly) anti-Robinson forms.

    • Intended to provide an applied documentation source for a collection of M-files of use to applied statisticians and data analysts, as well as bioinformaticians, chemometricians, and psychometricians
    • Industrial engineers, quantitative psychologists, and behavioral and social scientists will also find the content of this book beneficial
    • Two kinds of proximity information are analyzed throughout the book: one-mode and two-mode
    Read more

    Reviews & endorsements

    'The Structural Representation of Proximity Matrices with MATLAB combines state of the art proximity matrix representation with a modern programming language, making previously inaccessible techniques accessible to the general user. The material is not just a recapitulation of well-known techniques, but an insightful book that could only have been written by experts in the field. In short, this book fills a major gap in the literature.' Douglas L. Steinley, Assistant Professor, University of Missouri

    Customer reviews

    Not yet reviewed

    Be the first to review

    Review was not posted due to profanity

    ×

    , create a review

    (If you're not , sign out)

    Please enter the right captcha value
    Please enter a star rating.
    Your review must be a minimum of 12 words.

    How do you rate this item?

    ×

    Product details

    • Date Published: March 2006
    • format: Paperback
    • isbn: 9780898716078
    • length: 184 pages
    • dimensions: 255 x 177 x 12 mm
    • weight: 0.407kg
    • availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
  • Table of Contents

    Preface
    Part I. (Multi- and Unidimensional) City-Block Scaling:
    1. Linear unidimensional scaling
    2. Linear multidimensional scaling
    3. Circular scaling
    4. LUS for two-mode proximity data
    Part II. The Representation of Proximity Matrices by Tree Structures:
    5. Ultrametrics for symmetric proximity data
    6. Additive trees for symmetric proximity data
    7. Fitting multiple tree structures to a symmetric sroximity matrix
    8. Ultrametrics and additive trees for two-mode (rectangular) proximity data
    Part III. The Representation of Proximity Matrices by Structures Dependent on Order (Only):
    9. Anti-Robinson matrices for symmetric proximity data
    10. Circular anti-Robinson matrices for symmetric proximity data
    11. Anti-Robinson matrices for two-mode proximity data
    Appendix
    Bibliography
    Indices.

  • Authors

    Lawrence Hubert, University of Illinois, Urbana-Champaign

    Phipps Arabie, Rutgers University, New Jersey

    Jacqueline Meulman, Universiteit Leiden

Related Books

Sorry, this resource is locked

Please register or sign in to request access. If you are having problems accessing these resources please email lecturers@cambridge.org

Register Sign in
Please note that this file is password protected. You will be asked to input your password on the next screen.

» Proceed

You are now leaving the Cambridge University Press website. Your eBook purchase and download will be completed by our partner www.ebooks.com. Please see the permission section of the www.ebooks.com catalogue page for details of the print & copy limits on our eBooks.

Continue ×

Continue ×

Continue ×
warning icon

Turn stock notifications on?

You must be signed in to your Cambridge account to turn product stock notifications on or off.

Sign in Create a Cambridge account arrow icon
×

Find content that relates to you

Join us online

This site uses cookies to improve your experience. Read more Close

Are you sure you want to delete your account?

This cannot be undone.

Cancel

Thank you for your feedback which will help us improve our service.

If you requested a response, we will make sure to get back to you shortly.

×
Please fill in the required fields in your feedback submission.
×