Patrice Bertrand – författare

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.

By inviting me to write a preface, the organizers of the event in honour of Edwin Diday, have expressed their a?ection and I appreciate this very much. This gives me an opportunity to express my friendship and admiration for Edwin Diday, and I wrote this foreword with pleasure. My ?rst few meetings withEdwinDidaydatebackto1965through1975,daysofthedevelopmentof French statistics. This was a period when access to computers revolutionized the practice of statistics. This does not refer to individual computers or to terminals that have access to powerful networks. This was the era of the ?rst university calculation centres that one accessed over a counter. One would deposit cards on which program and data were punched in and come back a few hours or days later for the results. Like all those who used linear data analysis, the computer enabled me to calculate for each data set the value of mathematical objects (eigenvalues and eigenvectors for example) whose optimality properties had been demonstrated by mathematicians. It was - ready a big step to be able to do this in concrete experimental situations. With Dynamic Clustering Algorithm, Edwin Diday allowed us to discover that computers could be more than just a way of giving numerical values to known mathematical objects. Besides the e?ciency of the solutions he built, he led us to integrate the access to computers di?erently in the research and practice of data analysis.

By inviting me to write a preface, the organizers of the event in honour of Edwin Diday, have expressed their a?ection and I appreciate this very much. This gives me an opportunity to express my friendship and admiration for Edwin Diday, and I wrote this foreword with pleasure. My ?rst few meetings withEdwinDidaydatebackto1965through1975,daysofthedevelopmentof French statistics. This was a period when access to computers revolutionized the practice of statistics. This does not refer to individual computers or to terminals that have access to powerful networks. This was the era of the ?rst university calculation centres that one accessed over a counter. One would deposit cards on which program and data were punched in and come back a few hours or days later for the results. Like all those who used linear data analysis, the computer enabled me to calculate for each data set the value of mathematical objects (eigenvalues and eigenvectors for example) whose optimality properties had been demonstrated by mathematicians. It was - ready a big step to be able to do this in concrete experimental situations. With Dynamic Clustering Algorithm, Edwin Diday allowed us to discover that computers could be more than just a way of giving numerical values to known mathematical objects. Besides the e?ciency of the solutions he built, he led us to integrate the access to computers di?erently in the research and practice of data analysis.