Data Science, Learning by Latent Structures, and Knowledge Discovery
Editors: Lausen, Berthold, Krolak-Schwerdt, Sabine, Böhmer, Matthias (Eds.)
Free Preview- 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
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- About this book
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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.
- Reviews
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“The book under review is strongly recommended to interested theoreticians with strong background in measure and probability theory. Each chapter of the book is accompanied by supportive literature.” (Iris Burkholder, (Saarbrücken), British Journal of Hospital Medicine, Vol. 79 (5), May, 2018)
- Table of contents (48 chapters)
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Modernising Official Statistics: A Complex Challenge
Pages 3-11
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A New Supervised Classification of Credit Approval Data via the Hybridized RBF Neural Network Model Using Information Complexity
Pages 13-27
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Finding the Number of Disparate Clusters with Background Contamination
Pages 29-42
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Clustering of Solar Irradiance
Pages 43-53
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Factor Analysis of Local Formalism
Pages 57-67
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Table of contents (48 chapters)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Data Science, Learning by Latent Structures, and Knowledge Discovery
- Editors
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- Berthold Lausen
- Sabine Krolak-Schwerdt
- Matthias Böhmer
- Series Title
- Studies in Classification, Data Analysis, and Knowledge Organization
- Copyright
- 2015
- Publisher
- Springer-Verlag Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
- eBook ISBN
- 978-3-662-44983-7
- DOI
- 10.1007/978-3-662-44983-7
- Softcover ISBN
- 978-3-662-44982-0
- Series ISSN
- 1431-8814
- Edition Number
- 1
- Number of Pages
- XXII, 560
- Number of Illustrations
- 89 b/w illustrations, 56 illustrations in colour
- Topics
