rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Data Science
    sub title: Innovative Developments in Data Analysis and Clustering
  name:
    name part: Palumbo, Francesco.
    role:
      role term: editor.
  name:
    name part: Montanari, Angela.
    role:
      role term: editor.
  name:
    name part: Vichi, Maurizio.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2017
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVI, 342 p. 50 illus., 39 illus. in color. online resource.
  abstract: This edited volume on the latest advances in data science covers a wide range of topics in the context of data analysis and classification. In particular, it includes contributions on classification methods for high-dimensional data, clustering methods, multivariate statistical methods, and various applications. The book gathers a selection of peer-reviewed contributions presented at the Fifteenth Conference of the International Federation of Classification Societies (IFCS2015), which was hosted by the Alma Mater Studiorum, University of Bologna, from July 5 to 8, 2015.
  table of contents: Preface -- Part I: Classification Methods for High-Dimensional Data -- Scientific Contributions -- Part II: Clustering Methods and Applications -- Scientific Contributions -- Part III: Multivariate Methods and Applications -- Scientific Contributions.
  note: edited by Francesco Palumbo, Angela Montanari, Maurizio Vichi.
  subject:
    topic: Statistics
  subject:
    topic: Data mining
  subject:
    topic: Statistics
  subject:
    topic: Statistical Theory and Methods
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Statistics and Computing/Statistics Programs
  subject:
    topic: Big Data
  subject:
    topic: Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
  subject:
    topic: Statistics for Business/Economics/Mathematical Finance/Insurance
  classification: QA276-280
  classification: 519.5
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Studies in Classification, Data Analysis, and Knowledge Organization
  identifier: 9783319557236
  identifier: http://dx.doi.org/10.1007/978-3-319-55723-6
  record info:
    record creation date: 170704
    record change date: 20170704170850.0
    record identifier: 978-3-319-55723-6