Correspondence Analysis and Data Coding with Java and R

Correspondence Analysis and Data Coding with Java and R

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Developed by Jean-Paul Benzerci 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 Benzecri 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.show more

Product details

  • Hardback | 256 pages
  • 157.5 x 248.9 x 20.3mm | 498.96g
  • Taylor & Francis Ltd
  • London, United Kingdom
  • English
  • 56 black & white illustrations, 15 black & white tables
  • 1584885289
  • 9781584885283
  • 2,458,660

Table of contents

INTRODUCTION Data Analysis Notes on the History of Data Analysis Correspondence Analysis or Principal Components Analysis R Software for Correspondence Analysis and Clustering THEORY OF CORRESPONDENCE ANALYSIS Vectors and Projections Factors Transform Algebraic Perspective Clustering Questions Further R Software for Correspondence Analysis Summary INPUT DATA CODING Introduction 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 EXAMPLES AND CASE STUDIES 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 CONTENT ANALYSIS OF TEXT Introduction 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 References Indexshow more

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