書誌事項

Data analysis, machine learning and knowledge discovery

Myra Spiliopoulou, Lars Schmidt-Thieme, Ruth Janning, editors

(Studies in classification, data analysis, and knowledge organization)

Springer, c2014

  • : [pbk.]

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注記

"This volume contains the revised versions of selected papers presented during the 36th Annual Conference (GfKl 2012) of the German Classification Society (Gesellschaft für Classification (sic.)-GfKl) ... hosted by the University of Hildesheim, Germany, together with the Otto-von-Guericke-University of Magdeburg, Germany, in August 2012."--Pref

Includes bibliographical references and index

内容説明・目次

内容説明

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.

目次

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.

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詳細情報

  • NII書誌ID(NCID)
    BB16957097
  • ISBN
    • 9783319015941
  • LCCN
    2013954389
  • 出版国コード
    sz
  • タイトル言語コード
    eng
  • 本文言語コード
    eng
  • 出版地
    Cham
  • ページ数/冊数
    xxi, 470 p.
  • 大きさ
    24 cm
  • 親書誌ID
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