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Data Analysis, Machine Learning and Knowledge Discovery [electronic resource] / edited by Myra Spiliopoulou, Lars Schmidt-Thieme, Ruth Janning.

Contributor(s): Material type: TextTextSeries: Studies in Classification, Data Analysis, and Knowledge OrganizationPublisher: Cham : Springer International Publishing : Imprint: Springer, 2014Edition: 1st ed. 2014Description: XXI, 470 p. 120 illus., 32 illus. in color. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783319015958
Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification:
  • 519.5
LOC classification:
  • QA276-280
Online resources:
Contents:
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.
In: Springer Nature eBookSummary: 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.
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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.

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