Lausen, B and Krolak-Schwerdt, S and Böhmer, M (2015) Data science, learning by latent structures, and knowledge discovery. Studies in Classification, Data Analysis, and Knowledge Organization, 48 . Springer, pp. 1-22. ISBN 978-3-662-44982-0. Official URL: https://doi.org/10.1007/978-3-662-44983-7
Lausen, B and Krolak-Schwerdt, S and Böhmer, M (2015) Data science, learning by latent structures, and knowledge discovery. Studies in Classification, Data Analysis, and Knowledge Organization, 48 . Springer, pp. 1-22. ISBN 978-3-662-44982-0. Official URL: https://doi.org/10.1007/978-3-662-44983-7
Lausen, B and Krolak-Schwerdt, S and Böhmer, M (2015) Data science, learning by latent structures, and knowledge discovery. Studies in Classification, Data Analysis, and Knowledge Organization, 48 . Springer, pp. 1-22. ISBN 978-3-662-44982-0. Official URL: https://doi.org/10.1007/978-3-662-44983-7
Abstract
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.
| Item Type: | Book |
|---|---|
| Additional Information: | European Conference on Data Analysis, Luxembourg, July, 2013, Series: Studies in Classification, Data Analysis, and Knowledge Organization |
| Subjects: | H Social Sciences > HA Statistics H Social Sciences > HF Commerce Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Science and Health Faculty of Science and Health > Mathematics, Statistics and Actuarial Science, School of |
| SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
| Depositing User: | Unnamed user with email elements@essex.ac.uk |
| Date Deposited: | 05 Sep 2014 11:07 |
| Last Modified: | 05 Dec 2024 19:15 |
| URI: | http://repository.essex.ac.uk/id/eprint/9968 |