This first systematic and self-contained monograph on "Symbolic Data Analysis" presents the most recent methods for analyzing and visualizing symbolic data. It generalizes classical methods of exploratory, statistical and graphical data analysis to the case of complex data where the entries of a data table are, e. g., sets of categories or of numbers, intervals or probability distributions. Typical methods include: graphical displays using Zoom Stars, visualization and feature extraction by symbolic factor analysis, decision trees, discrimination, classification and clustering methods. Several benchmark examples from National Statistical Offices illustrate the usefulness of the methods. The book contains an extensive bibliography and a subject index.

This book brings together a wide range of topics and perspectives in the growing field of Classification and related methods of Exploratory and Multivariate Data Analysis. It gives a broad view on the state ofthe art, useful for those in the scientific community who gather data and seek tools for analyzing and interpreting large sets of data. As it presents a wide field of applications, this book is not only of interest for data analysts, mathematicians and statisticians, but also for scientists from many areas and disciplines concerned with real data, e. g. , medicine, biology, astronomy, image analysis, pattern recognition, social sciences, psychology, marketing, etc. It contains 79 invited or selected and refereed papers presented during the Fourth Bi- ennial Conference of the International Federation of Classification Societies (IFCS'93) held in Paris. Previous conferences were held at Aachen (Germany), Charlottesville (USA) and Edinburgh (U. K. ). The conference at Paris emerged from the elose coop- eration between the eight members of the IFCS: British Classification Society (BCS), Classification Society of North America (CSNA), Gesellschaft fur Klassifikation (GfKl), J apanese Classification Society (J CS), Jugoslovenska Sekcija za Klasifikacije (JSK), Societe Francophone de Classification (SFC), Societa. Italiana di Statistica (SIS), Vereniging voor Ordinatie en Classificatie (VOC), and was organized by INRIA ("Institut National de Recherche en Informatique et en Automatique"), Rocquencourt and the "Ecole Nationale Superieure des Telecommuni- cations," Paris.

Since its publication in 1976, the original volume has been warmly received. We have decided to put out this updated paperback edition so that the book can be more accessible to students. This paperback edition is essentially the same as the original hardcover volume except for the addition of a new chapter (Chapter 7) which reviews the recent advances in pattern recognition and image processing. Because of the limitations of length, we can only report the highlights and point the readers to the literature. A few typographical errors in the original edition were corrected. We are grateful to the National Science Foundation and the Office of Naval Research for supporting the editing of this book as well as the work described in Chapter 4 and a part of Chapter 7. West Lafayette, Indiana March 1980 K. S. Fu Preface to the First Edition During the past fifteen years there has been a considerable growth of interest in problems of pattern recognition. Contributions to the blossom of this area have come from many disciplines, including statistics, psychology, linguistics, computer science, biology, taxonomy, switching theory, communication theory, control theory, and operations research. Many different approaches have been proposed and a number of books have been published. Most books published so far deal with the decision-theoretic (or statistical) approach or the syntactic (or linguistic) is still far from its maturity, many approach.