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: Survey of Text Mining
    sub title: Clustering, Classification, and Retrieval
  name:
    name part: Berry, Michael W.
    role:
      role term: creator
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: xxu
    date issued: 2004
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (xvii, 244 pages)
  abstract: As the volume of digitized textual information continues to grow, so does the critical need for designing robust and scalable indexing and search strategies/software to meet a variety of user needs. Knowledge extraction or creation from text requires systematic, yet reliable processing that can be codified and adapted for changing needs and environments. Survey of Text Mining is a comprehensive edited survey organized into three parts: Clustering and Classification; Information Extraction and Retrieval; and Trend Detection. Many of the chapters stress the practical application of software and algorithms for current and future needs in text mining. Authors from industry provide their perspectives on current approaches for large-scale text mining and obstacles that will guide R&D activity in this area for the next decade. Topics and features: * Highlights issues such as scalability, robustness, and software tools * Brings together recent research and techniques from academia and industry * Examines algorithmic advances in discriminant analysis, spectral clustering, trend detection, and synonym extraction * Includes case studies in mining Web and customer-support logs for hot- topic extraction and query characterizations * Extensive bibliography of all references, including websites This useful survey volume taps the expertise of academicians and industry professionals to recommend practical approaches to purifying, indexing, and mining textual information. Researchers, practitioners, and professionals involved in information retrieval, computational statistics, and data mining, who need the latest text-mining methods and algorithms, will find the book an indispensable resource.
  table of contents: Clustering & Classification: Cluster-preserving dimension reduction methods for efficient classification of text data -- Automatic discovery of similar words -- Simultaneous clustering and dynamic keyword weighting for text documents -- Feature selection and document clustering; II: Information Extraction & Retrieval: Vector space models for search and cluster mining -- HotMiner?Discovering hot topics from dirty text -- Combining families of information retrieval algorithms using meta-learning; III: Trend Detection: Trend and behavior detection from Web queries -- A survey of emerging trend detection in textual data mining -- Index.
  note: edited by Michael W. Berry.
  note: Electronic resource.
  subject:
    topic: Computer science
  subject:
    topic: Information systems
  subject:
    topic: Information storage and retrieval systems
  subject:
    topic: Multimedia systems
  subject:
    topic: Mathematics
  classification: QA76.575
  classification: 006.7
  identifier: 9781475743050 (electronic bk.)
  identifier: 147574305X (electronic bk.)
  identifier: 9781441930576
  identifier: 1441930574
  identifier: 147574305X
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: AU@
    record creation date: 130125
    record change date: 20260422220407.0
    record identifier: in00003560100
    language of cataloging:
      language term: eng