Editors:
- Covers theory, methods and applications of data analysis
- Inspires for further research in the fields of Data Analysis, Learning by Latent Structures and Knowledge Discovery
- Combines the intensive work of excellent researchers from different disciplines united under the roof of data analysis
Part of the book series: Studies in Classification, Data Analysis, and Knowledge Organization (STUDIES CLASS)
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Table of contents (48 papers)
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Front Matter
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Invited Papers
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Front Matter
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Data Science and Clustering
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Front Matter
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About this book
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 methods; strategies for modeling complex data and mining large data sets; applications of advanced methods in specific domains of practice. The contributions offer interesting applications to various disciplines such as psychology, biology, medical and health sciences; economics, marketing, banking and finance; engineering; geography and geology; archeology, sociology, educational sciences, linguistics and musicology; library science. The book contains the selected and peer-reviewed papers presented during the European Conference on Data Analysis (ECDA 2013) which was jointly held by the German Classification Society (GfKl) and the French-speaking Classification Society (SFC) in July 2013 at the University of Luxembourg.
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Editors and Affiliations
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University of Essex, Colchester, United Kingdom
Berthold Lausen
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University of Luxembourg, Walferdange, Luxembourg
Sabine Krolak-Schwerdt, Matthias Böhmer
Bibliographic Information
Book Title: Data Science, Learning by Latent Structures, and Knowledge Discovery
Editors: Berthold Lausen, Sabine Krolak-Schwerdt, Matthias Böhmer
Series Title: Studies in Classification, Data Analysis, and Knowledge Organization
DOI: https://doi.org/10.1007/978-3-662-44983-7
Publisher: Springer Berlin, Heidelberg
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2015
Softcover ISBN: 978-3-662-44982-0Published: 18 May 2015
eBook ISBN: 978-3-662-44983-7Published: 06 May 2015
Series ISSN: 1431-8814
Series E-ISSN: 2198-3321
Edition Number: 1
Number of Pages: XXII, 560
Number of Illustrations: 89 b/w illustrations, 56 illustrations in colour
Topics: Statistics, general, Marketing, Psychometrics, Biostatistics, Data Mining and Knowledge Discovery, Operations Research/Decision Theory