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: Outlier Detection: Techniques and Applications
    sub title: A Data Mining Perspective
  name:
    name part: Ranga Suri, N. N. R.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Murty M, Narasimha.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Athithan, G.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXII, 214 p. 48 illus., 3 illus. in color. online resource.
  abstract: This book, drawing on recent literature, highlights several methodologies for the detection of outliers and explains how to apply them to solve several interesting real-life problems. The detection of objects that deviate from the norm in a data set is an essential task in data mining due to its significance in many contemporary applications. More specifically, the detection of fraud in e-commerce transactions and discovering anomalies in network data have become prominent tasks, given recent developments in the field of information and communication technologies and security. Accordingly, the book sheds light on specific state-of-the-art algorithmic approaches such as the community-based analysis of networks and characterization of temporal outliers present in dynamic networks. It offers a valuable resource for young researchers working in data mining, helping them understand the technical depth of the outlier detection problem and devise innovative solutions to address related challenges. .
  table of contents: Introduction -- Outlier Detection -- Research Issues in Outlier Detection -- Computational Preliminaries -- Outlier Detection in Categorical Data -- Outliers in High Dimensional Data.
  note: by N. N. R. Ranga Suri, Narasimha Murty M, G. Athithan.
  subject:
    topic: Computational intelligence
  subject:
    topic: Data mining
  subject:
    topic: Sociophysics
  subject:
    topic: Econophysics
  subject:
    topic: Computational Intelligence
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Data-driven Science, Modeling and Theory Building
  classification: Q342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Intelligent Systems Reference Library, 155
  identifier: 9783030051273
  identifier: https://doi.org/10.1007/978-3-030-05127-3
  record info:
    record creation date: 190110
    record change date: 20191027122937.0
    record identifier: 978-3-030-05127-3