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: Data Mining for Association Rules and Sequential Patterns
    sub title: Sequential and Parallel Algorithms
  name:
    name part: Adamo, Jean-Marc.
    role:
      role term: creator
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: nyu
    date issued: 2001
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (x, 254 pages 54 illustrations)
  abstract: The book provides a unified presentation of algorithms for association rule and sequential pattern discovery. For both mining problems, the presentation relies on the lattice structure of the search space. All algorithms are built as processes running on this structure. Proving their properties takes advantage of the mathematical properties of the structure. Mining for association rules and sequential patterns is known to be a problem with large computational complexity. The issue of designing efficient parallel algorithms should be considered as critical. Most algorithms in the book are devised for both sequential and parallel execution. Parallel algorithm design takes advantage of the lattice structure of the search space. Partitioning is performed via lattice recursive bisection. Database partitioning is also used as an additional source of parallelism. Part of the motivation for writing this book was postgraduate teaching. Since the book only assumes elementary mathematical knowledge in the domains of lattices, combinatorial optimization, probability calculus, and statistics, it is fit for use by undergraduate students as well. The algorithms are described in a C-like pseudo programming language. The computations are shown in great detail. This makes the book also fit for use by implementers: computer scientists in many domains as well as industry engineers.
  table of contents: Introduction -- Search Space Partition-Based Rule Mining -- Apriori and Other Algorithms -- Mining for Rules Over Attribute Taxonomies -- Constraint-Based Rule Mining -- Data Partition-Based Rule Mining -- Mining Rules with Categorical and Metric Attributes -- Optimizing Rules with Quantitative Attributes -- Beyond Support-Confidence Framework -- Sequential Patterns: Search Space Partition-Based Mining -- Appendix -- References -- Index.
  note: by Jean-Marc Adamo.
  note: Electronic resource.
  subject:
    topic: Computer science
  subject:
    topic: Computer software
  subject:
    topic: Database management
  subject:
    topic: Information storage and retrieval systems
  subject:
    topic: Artificial intelligence
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computer science
  subject:
    topic: Computer software
  subject:
    topic: Database management
  subject:
    topic: Information storage and retrieval systems
  classification: QA76.9.D3
  classification: 005.74
  identifier: 9781461300854 (electronic bk.)
  identifier: 1461300851 (electronic bk.)
  identifier: 9781461265115
  identifier: 1461265118
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: AU@
    record creation date: 121227
    record change date: 20260422161016.7
    record identifier: in00003569150
    language of cataloging:
      language term: eng