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: Mining Sequential Patterns from Large Data Sets
  name:
    name part: Wang, Wei.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Yang, Jiong.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2005
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XV, 163 p. online resource.
  abstract: The focus of Mining Sequential Patterns from Large Data Sets is on sequential pattern mining. In many applications, such as bioinformatics, web access traces, system utilization logs, etc., the data is naturally in the form of sequences. This information has been of great interest for analyzing the sequential data to find its inherent characteristics. Examples of sequential patterns include but are not limited to protein sequence motifs and web page navigation traces. To meet the different needs of various applications, several models of sequential patterns have been proposed. This volume not only studies the mathematical definitions and application domains of these models, but also the algorithms on how to effectively and efficiently find these patterns. Mining Sequential Patterns from Large Data Sets provides a set of tools for analyzing and understanding the nature of various sequences by identifying the specific model(s) of sequential patterns that are most suitable. This book provides an efficient algorithm for mining these patterns. Mining Sequential Patterns from Large Data Sets is designed for a professional audience of researchers and practitioners in industry and also suitable for graduate-level students in computer science. .
  table of contents: Related Work -- Periodic Patterns -- Statistically Significant Patterns -- Approximate Patterns -- Conclusion Remark.
  note: by Wei Wang, Jiong Yang.
  subject:
    topic: Computer science
  subject:
    topic: Computer communication systems
  subject:
    topic: Data structures (Computer science)
  subject:
    topic: Database management
  subject:
    topic: Data mining
  subject:
    topic: Information storage and retrieval
  subject:
    topic: Multimedia information systems
  subject:
    topic: Computer Science
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Database Management
  subject:
    topic: Information Storage and Retrieval
  subject:
    topic: Data Structures
  subject:
    topic: Multimedia Information Systems
  subject:
    topic: Computer Communication Networks
  classification: QA76.9.D343
  classification: 006.312
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Advances in Database Systems, 28
  identifier: 9780387242477
  identifier: http://dx.doi.org/10.1007/b104937
  record info:
    record creation date: 100301
    record change date: 20151029221332.0
    record identifier: 978-0-387-24247-7