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: Quality Measures in Data Mining
  name:
    name part: Guillet, Fabrice J.
    role:
      role term: editor.
  name:
    name part: Hamilton, Howard J.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2007
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIV, 314 p. online resource.
  abstract: Data mining analyzes large amounts of data to discover knowledge relevant to decision making. Typically, numerous pieces of knowledge are extracted by a data mining system and presented to a human user, who may be a decision-maker or a data-analyst. The user is confronted with the task of selecting the pieces of knowledge that are of the highest quality or interest according to his or her preferences. Since this selection is sometimes a daunting task, designing quality and interestingness measures has become an important challenge for data mining researchers in the last decade. This volume presents the state of the art concerning quality and interestingness measures for data mining. The book summarizes recent developments and presents original research on this topic. The chapters include surveys, comparative studies of existing measures, proposals of new measures, simulations, and case studies. Both theoretical and applied chapters are included. Papers for this book were selected and reviewed for correctness and completeness by an international review committee.
  table of contents: Overviews on rule quality -- Choosing the Right Lens: Finding What is Interesting in Data Mining -- A Graph-based Clustering Approach to Evaluate Interestingness Measures: A Tool and a Comparative Study -- Association Rule Interestingness Measures: Experimental and Theoretical Studies -- On the Discovery of Exception Rules: A Survey -- From data to rule quality -- Measuring and Modelling Data Quality for Quality-Awareness in Data Mining -- Quality and Complexity Measures for Data Linkage and Deduplication -- Statistical Methodologies for Mining Potentially Interesting Contrast Sets -- Understandability of Association Rules: A Heuristic Measure to Enhance Rule Quality -- Rule quality and validation -- A New Probabilistic Measure of Interestingness for Association Rules, Based on the Likelihood of the Link -- Towards a Unifying Probabilistic Implicative Normalized Quality Measure for Association Rules -- Association Rule Interestingness: Measure and Statistical Validation -- Comparing Classification Results between N-ary and Binary Problems.
  note: edited by Fabrice J. Guillet, Howard J. Hamilton.
  subject:
    topic: Engineering
  subject:
    topic: Artificial intelligence
  subject:
    topic: Applied mathematics
  subject:
    topic: Engineering mathematics
  subject:
    topic: Engineering
  subject:
    topic: Appl.Mathematics/Computational Methods of Engineering
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  classification: TA329-348
  classification: TA640-643
  classification: 519
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Studies in Computational Intelligence, 43
  identifier: 9783540449188
  identifier: http://dx.doi.org/10.1007/978-3-540-44918-8
  record info:
    record creation date: 100301
    record change date: 20151204164558.0
    record identifier: 978-3-540-44918-8