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
  title info:
    sub title: Sequential and parallel algorithms
  title info: title
  name:
    name part: Adamo, Jean-Marc
    role:
      role term: creator
  type of resource: text
  origin info:
    place:
      place term: xx
    place:
      place term: New York
    publisher: Springer
    date issued: c2001
    date issued: 9999
    issuance: monographic
  language:
    language term: und
  physical description:
    form: print
  abstract: Recent advances in the data collection and storage technologies have made it possible for companies (e.g. bar-code technology), administrative agencies (e.g. census data) and scientific laboratories (e.g. molecule databases in chemistry or biology) to keep vast amounts of data relating to their activities. At the same time, the availability of cheap computing power has made automatic extraction of structured knowledge from these data feasible. Such an activity is referred to as data mining. More recently, the advent on the marketplace of cheap high performance (gigabit level) communication switches is even placing cheap parallel data mining within the reach of the majority. Data mining includes such activities as classification, clustering, similarity analysis, summarization, association rule and sequential pattern discovery, and so forth. This state-of-the-art monograph focuses on two key topics: association rules and sequential pattern discovery.
  note: Jean-Marc Adamo
  subject:
    topic: Data mining,Computer algorithms,Khai dữ liệu,Thuật toán máy tính
  classification: 006.3
  classification: A199
  record info:
    record creation date: 210402
    record identifier: CTU_153238