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  • 标题:ARDV: A New Density Based Outlier Mining Approach
  • 本地全文:下载
  • 作者:Krishna Gopal Sharma ; Govind Jha ; Akash Yadav
  • 期刊名称:International Journal of Computer Science & Technology
  • 印刷版ISSN:2229-4333
  • 电子版ISSN:0976-8491
  • 出版年度:2013
  • 卷号:4
  • 期号:1
  • 页码:103-105
  • 语种:English
  • 出版社:Ayushmaan Technologies
  • 摘要:Local Outlier Factor (LOF) is an important and well known density based outliers handling algorithm, which quantifies, how much an object is outlying, in a given database. In this paper first we discuss LOF then we introduce the concept of ARDV. In LOF there is a concept of lrd (local reachability density). If in place of lrd we calculate ard (average reachability distance) and in place of LOF we calculate variance in ard (ARDV) then experimental results show that percentage of detecting correct outliers increases without increasing time complexity.
  • 关键词:Outlier-ness;local reachability density;MinPts- Neighborhood; ard;ARDV
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