• DocumentCode
    3519340
  • Title

    Discovering Local Outlier Based on Rough Clustering

  • Author

    Mi, Hongjuan

  • Author_Institution
    Inf. Eng. Sch., Lanzhou Commercial Coll., Lanzhou, China
  • fYear
    2011
  • fDate
    28-29 May 2011
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    The density at a data point is defined based on kernel function. And we introduce weight to refine rough k-means algorithm. Then we construct the formula for calculating local outlier score based on the clusters generated by the refined rough k-means algorithm. We use a synthetic data set and a real-world data set to verify that the new technique for local outliers detection is not only accurate but also efficient.
  • Keywords
    data mining; pattern clustering; statistical analysis; kernel function; local outlier discovery; rough clustering; rough k-means algorithm; Algorithm design and analysis; Approximation algorithms; Approximation methods; Clustering algorithms; Data mining; Kernel; Rough sets;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Systems and Applications (ISA), 2011 3rd International Workshop on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-9855-0
  • Electronic_ISBN
    978-1-4244-9857-4
  • Type

    conf

  • DOI
    10.1109/ISA.2011.5873272
  • Filename
    5873272
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3519340