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Titlebook: Data Science, Learning by Latent Structures, and Knowledge Discovery; Berthold Lausen,Sabine Krolak-Schwerdt,Matthias B? Conference procee

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書目名稱Data Science, Learning by Latent Structures, and Knowledge Discovery
編輯Berthold Lausen,Sabine Krolak-Schwerdt,Matthias B?
視頻videohttp://file.papertrans.cn/264/263141/263141.mp4
概述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
叢書名稱Studies in Classification, Data Analysis, and Knowledge Organization
圖書封面Titlebook: Data Science, Learning by Latent Structures, and Knowledge Discovery;  Berthold Lausen,Sabine Krolak-Schwerdt,Matthias B? Conference procee
描述.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..
出版日期Conference proceedings 2015
關鍵詞Classification; Data Analysis; Data Science; Data Stream; Knowledge Organization; Latent Structures
版次1
doihttps://doi.org/10.1007/978-3-662-44983-7
isbn_softcover978-3-662-44982-0
isbn_ebook978-3-662-44983-7Series ISSN 1431-8814 Series E-ISSN 2198-3321
issn_series 1431-8814
copyrightSpringer-Verlag Berlin Heidelberg 2015
The information of publication is updating

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Jesica de Armas,Helena Ramalhinho,Stefan Vo? similarity/dissimilarity measures: a similarity measure between concepts (elements) of a lattice and a dissimilarity measure between concept lattices defined on the same set of objects and attributes. Both measures are based on the overhanging relation previously introduced by the author, which are a cryptomorphism of lattices.
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Berthold Lausen,Sabine Krolak-Schwerdt,Matthias B?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
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Arturo Pérez Rivera,Martijn Mesta. The prospective clusters can readily be distinguished from background noise and from other forms of outliers. A confirmatory Forward Search, involving control on the sizes of statistical tests, establishes precise cluster membership. The method performs as well as robust methods such as TCLUST.
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