• DocumentCode
    1957417
  • Title

    Patterns in large numerical data

  • Author

    Lin, Tsau Young

  • Author_Institution
    Dept. of Comput. Sci., San Jose State Univ., CA, USA
  • fYear
    2002
  • fDate
    2002
  • Firstpage
    306
  • Lastpage
    309
  • Abstract
    For time series data, the interest is in "vertical" patterns, not "horizontal" associations; in other words, the focus is on patterns of a large (long) numerical sequence (of vectors or numbers). This paper is theoretical; all data has no noise. It searches for several important mathematical concepts in data mining, such as pattern and prediction and the notion of large. It proposes that data is large if the complexity of data is more than the complexity of the pattern, and reconfirms the previous proposal that a pattern\´s complexity should be smaller than data complexity.
  • Keywords
    data mining; database theory; sequences; time series; very large databases; complexity; data mining; large numerical data; numbers; patterns; prediction; time series data; vectors; Algebra; Automation; Binary sequences; Computer science; Data mining; Databases; Information theory; Machine learning; Mathematics; Polynomials;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Information Processing Society, 2002. Proceedings. NAFIPS. 2002 Annual Meeting of the North American
  • Print_ISBN
    0-7803-7461-4
  • Type

    conf

  • DOI
    10.1109/NAFIPS.2002.1018075
  • Filename
    1018075