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: Unsupervised classification
    sub title: similarity measures, classical and metaheuristic approaches, and applications
  name:
    name part: Bandyopadhyay, Sanghamitra
    name part: 1968-
    role:
      role term: creator
    role:
      role term: author
  name:
    name part: Saha, Sriparna
    role:
      role term: author
  type of resource: text
  origin info:
    place:
      place term: nyu
    place:
      place term: New York
    publisher: Springer
    date issued: c2013
    date issued: 2013
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: print
    extent: xviii, 262 p. : ill. (some col.) ; 25 cm.
  abstract: Clustering is an important unsupervised classification technique where data points are grouped such that points that are similar in some sense belong to the same cluster. Cluster analysis is a complex problem as a variety of similarity and dissimilarity measures exist in the literature.This is the first book focused on clustering with a particular emphasis on symmetry-based measures of similarity and metaheuristic approaches. The aim is to find a suitable grouping of the input data set so that some criteria are optimized, and using this the authors frame the clustering problem as an optimization one where the objectives to be optimized may represent different characteristics such as compactness, symmetrical compactness, separation between clusters, or connectivity within a cluster. They explain the techniques in detail and outline many detailed applications in data mining, remote sensing and brain imaging, gene expression data analysis, and face detection.The book will be useful to graduate students and researchers in computer science, electrical engineering, system science, and information technology, both as a text and as a reference book. It will also be useful to researchers and practitioners in industry working on pattern recognition, data mining, soft computing, metaheuristics, bioinformatics, remote sensing, and brain imaging
  table of contents: 1. Introduction -- 2. Some single- and multiobjective optimization techniques -- 3. Similarity measures -- 4. Clustering algorithms -- 5. Point symmetry-based distance measures and their applications to clustering -- 6. A validity index based on symmetry: application to satellite image segmentation -- 7. Symmetry-based automatic clustering -- 8. Some line symmetry distance-based clustering techniques -- 9. Use of multiobjective optimization for data clustering
  note: Sanghamitra Bandyopadhyay, Sriparna Saha
  note: Includes bibliographical references (p. 245-257) and index
  subject:
    topic: Cluster analysis
    topic: Data processing
  subject:
    topic: Pattern perception
  classification: Q327 .B36 2013
  identifier: 3642324509 (hardback : alk. paper)
  identifier: 3642324517 (ebook)
  identifier: 9783642324505 (hardback : alk. paper)
  identifier: 9783642324512 (ebook)
  record info:
    record creation date: 130530
    record change date: 20160612093000.0
    record identifier: 0000083593