• DocumentCode
    467708
  • Title

    Mining Negative Sequential Patterns in Transaction Databases

  • Author

    Ouyang, Wei-min ; Huang, Qin-hua

  • Author_Institution
    Shanghai Univ. of Sport, Shanghai
  • Volume
    2
  • fYear
    2007
  • fDate
    19-22 Aug. 2007
  • Firstpage
    830
  • Lastpage
    834
  • Abstract
    Sequential pattern is an important research topic in data mining and knowledge discovery. Sequential pattern is traditionally formed as (A, B) where A and B are frequent sequence in a transaction database. We extend this definition to include sequential patterns of forms (A, notB), (notA, B) and (notA, notB), which present negative sequential patterns among sequences. We call patterns of the form (A, B) positive sequential patterns, and patterns of the other forms negative patterns. Negative sequential patterns can also provide very useful insight view into the data set although they are different from positive ones. We put forward a discovery algorithm for mining negative sequential patterns from large transaction database in this paper.
  • Keywords
    data mining; data mining; frequent sequence; negative sequential patterns; transaction databases; Association rules; Conference management; Cybernetics; Data engineering; Data mining; Engineering management; Itemsets; Knowledge management; Machine learning; Transaction databases; Frequent sequence; Infrequent sequence; Negative sequential patterns; Sequential patterns;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2007 International Conference on
  • Conference_Location
    Hong Kong
  • Print_ISBN
    978-1-4244-0973-0
  • Electronic_ISBN
    978-1-4244-0973-0
  • Type

    conf

  • DOI
    10.1109/ICMLC.2007.4370257
  • Filename
    4370257