Correspondence Analysis and Data Coding with Java and R

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ISBN 9781584885283
Cat# C5289



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  • Introduces the theory, methods, and applications of correspondence analysis, with an emphasis on data coding
  • Exemplifies the importance of correspondence analysis in areas such as the analysis of time-evolving data and the analysis of text
  • Includes applications to financial and other time series analysis
  • Offers full discussion of software code in both Java and R
  • Provides software and data sets used in the book on a supporting web site:
  • Summary

    Developed by Jean-Paul Benzérci more than 30 years ago, correspondence analysis as a framework for analyzing data quickly found widespread popularity in Europe. The topicality and importance of correspondence analysis continue, and with the tremendous computing power now available and new fields of application emerging, its significance is greater than ever.

    Correspondence Analysis and Data Coding with Java and R clearly demonstrates why this technique remains important and in the eyes of many, unsurpassed as an analysis framework. After presenting some historical background, the author presents a theoretical overview of the mathematics and underlying algorithms of correspondence analysis and hierarchical clustering. The focus then shifts to data coding, with a survey of the widely varied possibilities correspondence analysis offers and introduction of the Java software for correspondence analysis, clustering, and interpretation tools. A chapter of case studies follows, wherein the author explores applications to areas such as shape analysis and time-evolving data. The final chapter reviews the wealth of studies on textual content as well as textual form, carried out by Benzécri and his research lab. These discussions show the importance of correspondence analysis to artificial intelligence as well as to stylometry and other fields.

    This book not only shows why correspondence analysis is important, but with a clear presentation replete with advice and guidance, also shows how to put this technique into practice. Downloadable software and data sets allow quick, hands-on exploration of innovative correspondence analysis applications.

    Table of Contents

    Data Analysis
    Notes on the History of Data Analysis
    Correspondence Analysis or Principal Components Analysis
    R Software for Correspondence Analysis and Clustering

    Vectors and Projections
    Algebraic Perspective
    Further R Software for Correspondence Analysis

    From Doubling to Fuzzy Coding and Beyond
    Assessment of Coding Methods
    The Personal Equation and Double Rescaling
    Case Study: DNA Exon and Intron Junction Discrimination
    Conclusions on Coding
    Java Software

    Introduction to Analysis of Size and Shape
    Comparison of Prehistoric and Modern Groups of Canids
    Craniometric Data from Ancient Egyptian Tombs
    Time-Varying Data Analysis: Examples from Economics
    Financial Modeling and Forecasting

    Correspondence Analysis
    Tool Words: Between Analysis of Form and Analysis of Content Towards Content Analysis
    Textual and Documentary Typology
    Conclusion: Methodology in Free Text Analysis
    Software for Text Processing
    Introduction to the Text Analysis Case Studies
    Eight Hypotheses of Parmenides Regarding the One
    Comparative Study of Reality, Fable and Dream
    Single Document Analysis
    Conclusion on Text Analysis Case Studies

    Concluding Remarks


    Author Bio(s)

    Editorial Reviews

    "Detailed examples of its application to data are drawn from an astonishingly wide variety of fields; astronomy, financial modeling and forecasting, comparisons of prehistoric and modern groups of dogs, ancient goblets and measurements on ancient Egyptian skulls. …All in all this book can be recommended as a succinct reference on all aspects of correspondence analysis, theoretical, computational, and practical."
    -J.M. Juritz, Short Book Reviews of the ISI

    "This book plays an important role in bridging the gap between learning a method and actually implementing it . . . could serve as either a text for an introductory course on CA or as a supplementary text to a more advanced graduate course in CA or multivariate techniques in general . . . The author should be commended for bringing these issues to the forefront."

    – Douglas Steinley, University of Missouri-Columbia, in Psychometrika, March 2007, Vol. 74, No. 1

    Downloads / Updates

    Resource OS Platform Updated Description Instructions
    Cross Platform April 11, 2005 Datasets and software can be found at the dedicated webpage for the book.