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 methods; strategies for modeling complex data...

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Bibliographic Details
Other Authors: Lausen, Berthold (Editor), Krolak-Schwerdt, Sabine (Editor), Böhmer, Matthias (Editor)
Format: eBook
Language:English
Published: Berlin, Heidelberg Springer Berlin Heidelberg 2015, 2015
Edition:1st ed. 2015
Series:Studies in Classification, Data Analysis, and Knowledge Organization
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Description
Summary: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
Physical Description:XXII, 560 p. 145 illus., 56 illus. in color online resource
ISBN:9783662449837