diday edwin (31 Ergebnisse)

Softcover. Zustand: Très bon. Ancien livre de bibliothèque. Edition 2000. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Edition 2000. Ammareal gives back up to 15% of this item's net price to charity organizations.

Zustand: Good. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some stamps and wear, but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions.

Softcover. Zustand: Fine. Leichte Risse; Gebogener Buchrücken.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. pp. 288.

Paperback. Zustand: Brand New. 468 pages. 9.25x6.25x1.06 inches. In Stock.

Hardcover. Zustand: Very good. Hardcover Quarto dust jacket. illustrated boards 480 pp Standard shipping (no tracking) / Priority (with tracking) / Custom quote for large or heavy orders.

Zustand: New. pp. 330 Illus.

Zustand: New. LYNNE BILLARD, PHD, is University Professor in the Department of Statistics at the University of Georgia, USA. She has over two hundred and twenty-five publications mostly in leading journals, and co-edited six books. Professor Billard is a former president.

Hardcover. Zustand: Brand New. 340 pages. 9.25x6.25x0.75 inches. In Stock.

Kartoniert / Broschiert. Zustand: New.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Zustand: Brand New. 1st edition. 693 pages. 9.50x6.50x1.75 inches. In Stock.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Raymond Bisdorff CRP-GL, Luxembourg The development of the SODAS software based on symbolic data analysis was extensively described in the previous chapters of this book. It was accompanied by a series of benchmark activities involving some official statistical institutes throughout Europe. Partners in these benchmark activities were the National Statistical Institute (INE) of Portugal, the Instituto Vasco de Estadistica Euskal (EUSTAT) from Spain, the Office For National Statistics (ONS) from the United Kingdom, the Inspection Generale de la Securite Sociale (IGSS) from Luxembourg 1 and marginally the University of Athens . The principal goal of these benchmark activities was to demonstrate the usefulness of symbolic data analysis for practical statistical exploitation and analysis of official statistical data. This chapter aims to report briefly on these activities by presenting some signifi cant insights into practical results obtained by the benchmark partners in using the SODAS software package as described in chapter 14 below.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 für 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.

Zustand: New. A systematic and self-contained monograph on "Symbolic Data Analysis", presenting several methods for analyzing and visualizing symbolic data. It generalizes classical methods of exploratory, statistical and graphical data analysis to the case of complex data. Editor(s): Bock, Hans-Hermann; Diday, E. Series: Studies in Classification, Data Analysis, and Knowledge Organization. Num Pages: 443 pages, biography. BIC Classification: KJQ; PBT; UNC. Category: (P) Professional & Vocational. Dimension: 242 x 170 x 23. Weight in Grams: 624. . 1999. Paperback. . . . . Books ship from the US and Ireland.

Zustand: Gut. 384 Seiten nice book ex Library Sprache: Englisch Gewicht in Gramm: 969 23,9 x 15,5 x 2,3 cm, Taschenbuch Auflage: Softcover reprint of the original 1st ed. 1996.

Zustand: New.

Zustand: New. pp. xi + 457 Illus.

Zustand: New. &Uumlber den AutorEdwin Diday is Emeritus Professor at Paris-Dauphine University-PSL. He helped to introduce the symbolic data analysis paradigm and the dynamic clustering method (opening the path to local models), as well as pyramid.

Hardcover. Zustand: Brand New. 234 pages. 9.50x6.25x0.75 inches. In Stock.

Gebunden. Zustand: New. Classical statistical techniques are often inadequate when it comes to analysing some of the large and internally variable datasets common today. Symbolic Data Analysis (SDA) has evolved in response to this problem and is a vital tool for summarizing inform.

Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 258 | Sprache: Englisch | Produktart: Bücher | Data science unifies statistics, data analysis and machine learning to achieve a better understanding of the masses of data which are produced today, and to improve prediction. Special kinds of data (symbolic, network, complex, compositional) are increasingly frequent in data science. These data require specific methodologies, but there is a lack of reference work in this field. Advances in Data Science fills this gap. It presents a collection of up-to-date contributions by eminent scholars following two international workshops held in Beijing and Paris. The 10 chapters are organized into four parts: Symbolic Data, Complex Data, Network Data and Clustering. They include fundamental contributions, as well as applications to several domains, including business and the social sciences.

Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 258 | Sprache: Englisch | Produktart: Bücher | Data science unifies statistics, data analysis and machine learning to achieve a better understanding of the masses of data which are produced today, and to improve prediction. Special kinds of data (symbolic, network, complex, compositional) are increasingly frequent in data science. These data require specific methodologies, but there is a lack of reference work in this field. Advances in Data Science fills this gap. It presents a collection of up-to-date contributions by eminent scholars following two international workshops held in Beijing and Paris. The 10 chapters are organized into four parts: Symbolic Data, Complex Data, Network Data and Clustering. They include fundamental contributions, as well as applications to several domains, including business and the social sciences.

Zustand: New. 2020. 1st Edition. Hardback. . . . . . Books ship from the US and Ireland.

Hardcover. Zustand: Brand New. illustrated edition. 476 pages. 8.00x9.00x1.00 inches. In Stock.

Zustand: New. Symbolic data analysis is a relatively new field that provides a range of methods for analyzing complex datasets. Standard statistical methods do not have the power or flexibility to make sense of very large datasets, and symbolic data analysis techniques have been developed in order to extract knowledge from such data. Editor(s): Diday, Edwin; Noirhomme-Fraiture, Monique. Num Pages: 476 pages, black & white illustrations, black & white line drawings, figures, charts, graphs. BIC Classification: PBT. Category: (P) Professional & Vocational. Dimension: 250 x 175 x 31. Weight in Grams: 962. . 2008. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.

Buch. Zustand: Neu. Neuware - Data science unifies statistics, data analysis and machine learning to achieve a better understanding of the masses of data which are produced today, and to improve prediction. Special kinds of data (symbolic, network, complex, compositional) are increasingly frequent in data science. These data require specific methodologies, but there is a lack of reference work in this field.

Buch. Zustand: Neu. Neuware - Classical statistical techniques are often inadequate when it comes to analysing some of the large and internally variable datasets common today. Symbolic Data Analysis (SDA) has evolved in response to this problem and is a vital tool for summarizing information in such a way that the resulting data is of a manageable size. Symbolic data, represented byintervals, lists, histograms, distributions, curves and the like, keeps the 'internal variation' of summaries better than standard data. SDA therefore plays a key role in the interaction between statistics and data processing, and has established itself as an important tool for analysing official statistics.Through an extension of the concepts employed in data mining, the Editors provide an advanced guide to the techniques required to analyse symbolic data. Contributions from leading experts in the field enable the reader to build models and make predictions about future events.The book:\* Provides new graphical tools for the interpretation of large data sets.\* Extends standard statistics, data analysis, data mining and knowledge discovery to symbolic data.\* Introduces the SODAS software, which is complementary to existing data analysis software (e.g. SAS, SPSS, SPAD) that are unable to work on symbolic data.\* Induces, exports, and compares knowledge from one database to another.\* Features a supporting website hosting the software, and user manual.Symbolic Data Analysis and the SODAS Software is primarily aimed at practitioners of symbolic data analysis, such as statisticians and economists, within both the public and private sectors. There is also much of interest to postgraduate students and researchers within web mining, text mining, and bioengineering.

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