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＜E-Book＞
Data Analysis, Machine Learning and Knowledge Discovery / edited by Myra Spiliopoulou, Lars Schmidt-Thieme, Ruth Janning
(Studies in Classification, Data Analysis, and Knowledge Organization. ISSN:21983321)

Edition	1st ed. 2014.
Publisher	Cham : Springer International Publishing : Imprint: Springer
Year	2014
Language	English
Size	XXI, 470 p. 120 illus., 32 illus. in color : online resource
Authors	Spiliopoulou, Myra editor Schmidt-Thieme, Lars editor Janning, Ruth editor SpringerLink (Online service)
Subjects	LCSH:Mathematical statistics -- Data processing  All Subject Search LCSH:Data mining LCSH:Marketing LCSH:Finance LCSH:Biometry LCSH:Psychology FREE:Statistics and Computing FREE:Data Mining and Knowledge Discovery FREE:Marketing FREE:Financial Economics FREE:Biostatistics FREE:Behavioral Sciences and Psychology
Notes	AREA Statistics and Data Analysis: Classifcation, Cluster Analysis, Factor Analysis and Model Selection -- AREA Machine Learning and Knowledge Discovery: Clustering, Classifiers, Streams and Social Networks -- AREA Data Analysis and Classification in Marketing -- AREA Data Analysis in Finance -- AREA Data Analysis in Biostatistics and Bioinformatics -- AREA Interdisciplinary Domains: Data Analysis in Music, Education and Psychology.- LIS Workshop: Workshop on Classification and Subject Indexing in Library and Information Science Data analysis, machine learning and knowledge discovery are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and text mining, marketing, medicine, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and knowledge discovery presented during the 36th annual conference of the German Classification Society (GfKl). The conference was held at the University of Hildesheim (Germany) in August 2012 HTTP:URL=https://doi.org/10.1007/978-3-319-01595-8

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