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Data science, learning by latent structures, and knowledge discovery

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.

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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 > Mathematical Sciences, Department of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 05 Sep 2014 11:07
Last Modified: 23 Sep 2022 19:11
URI: http://repository.essex.ac.uk/id/eprint/9968

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