Data Science, Learning by Latent Structures, and Knowledge Discovery
This volume comprises papers dedicated to data science and the extraction of knowledge from many types of data: structural, quantitative, or statistical approaches for the analysis of data; advances in classification, clustering, and pattern recognition m
- PDF / 32,062,690 Bytes
- 552 Pages / 439.43 x 666.14 pts Page_size
- 74 Downloads / 225 Views
Berthold Lausen Sabine Krolak-Schwerdt Matthias Böhmer Editors
Data Science, Learning by Latent Structures, and Knowledge Discovery
Studies in Classification, Data Analysis, and Knowledge Organization
Managing Editors
Editorial Board
H.-H. Bock, Aachen W. Gaul, Karlsruhe M. Vichi, Rome C. Weihs, Dortmund
D. Baier, Cottbus F. Critchley, Milton Keynes R. Decker, Bielefeld E. Diday, Paris M. Greenacre, Barcelona C.N. Lauro, Naples J. Meulman, Leiden P. Monari, Bologna S. Nishisato, Toronto N. Ohsumi, Tokyo O. Opitz, Augsburg G. Ritter, Passau M. Schader, Mannheim
More information about this series at http://www.springer.com/series/1564
Berthold Lausen Sabine Krolak-Schwerdt Matthias Böhmer
Editors
Data Science, Learning by Latent Structures, and Knowledge Discovery
123
Editors Berthold Lausen University of Essex Colchester United Kingdom
Sabine Krolak-Schwerdt University of Luxembourg Walferdange Luxembourg
Matthias Böhmer University of Luxembourg Walferdange Luxembourg
ISSN 1431-8814 Studies in Classification, Data Analysis, and Knowledge Organization ISBN 978-3-662-44982-0 ISBN 978-3-662-44983-7 (eBook) DOI 10.1007/978-3-662-44983-7 Library of Congress Control Number: 2015938905 Springer Heidelberg New York Dordrecht London © Springer-Verlag Berlin Heidelberg 2015 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Springer-Verlag GmbH Berlin Heidelberg is part of Springer Science+Business Media (www.springer.com)
Foreword
Dear Scholars, It is both my privilege and my pleasure to invite you to this volume and to get inspired for further research in the fields of Data Analysis, Learning by Latent Structures, and Knowledge Discovery. After having held the first European Conference on Data Analysis (ECDA) in Luxembourg in the year 2013, this book is the outcome of intensive work of excellent researchers from different disciplines united under the roof of data analysis. When the Gesellschaft für Klassifikation (GfKl) accepted Luxembourg’s offer to host the
