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: Advances in K-means Clustering
    sub title: A Data Mining Thinking
  name:
    name part: Wu, Junjie.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2012
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVI, 180 p. online resource.
  abstract: Nearly everyone knows K-means algorithm in the fields of data mining and business intelligence. But the ever-emerging data with extremely complicated characteristics bring new challenges to this "old" algorithm. This book addresses these challenges and makes novel contributions in establishing theoretical frameworks for K-means distances and K-means based consensus clustering, identifying the "dangerous" uniform effect and zero-value dilemma of K-means, adapting right measures for cluster validity, and integrating K-means with SVMs for rare class analysis. This book not only enriches the clustering and optimization theories, but also provides good guidance for the practical use of K-means, especially for important tasks such as network intrusion detection and credit fraud prediction. The thesis on which this book is based has won the "2010 National Excellent Doctoral Dissertation Award", the highest honor for not more than 100 PhD theses per year in China.
  table of contents: Cluster Analysis and K-means Clustering: An Introduction -- The Uniform Effect of K-means Clustering -- Generalizing Distance Functions for Fuzzy c-Means Clustering -- Information-Theoretic K-means for Text Clustering -- Selecting External Validation Measures for K-means Clustering -- K-means Based Local Decomposition for Rare Class Analysis -- K-means Based Consensus Clustering.
  note: by Junjie Wu.
  subject:
    topic: Computer science
  subject:
    topic: Information technology
  subject:
    topic: Business
    topic: Data processing
  subject:
    topic: Database management
  subject:
    topic: Data mining
  subject:
    topic: Statistics
  subject:
    topic: Computer Science
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Statistics for Business/Economics/Mathematical Finance/Insurance
  subject:
    topic: IT in Business
  subject:
    topic: Database Management
  classification: QA76.9.D343
  classification: 006.312
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Springer Theses, Recognizing Outstanding Ph.D. Research
  identifier: 9783642298073
  identifier: http://dx.doi.org/10.1007/978-3-642-29807-3
  record info:
    record creation date: 120709
    record change date: 20151204151609.0
    record identifier: 978-3-642-29807-3