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Volumn 2006, Issue , 2006, Pages 609-613

Fast mining of distance-based outliers in high-dimensional datasets

Author keywords

Approximate k nearest neighbors; Clustering; High dimensional data sets; Outlier detection

Indexed keywords

ALGORITHMS; COMPUTATIONAL METHODS; DATA STRUCTURES; INFORMATION MANAGEMENT;

EID: 33745440901     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972764.70     Document Type: Conference Paper
Times cited : (39)

References (12)
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  • 3
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    • S. Bay and M. Schwabacher. Mining distance-based outliers in near linear time with randomization and a simple pruning rule. In SIGKDD, 2003.
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    • Bay, S.1    Schwabacher, M.2
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    • Berchtold, S.1    Keim, D.2    Kreigel, H.3
  • 6
    • 70349954240 scopus 로고    scopus 로고
    • Fast mining of distance-based outliers in high dimensional datasets
    • CSE, The Ohio State University
    • A. Ghoting, S. Parthasarathy, and M. Otey. Fast mining of distance-based outliers in high dimensional datasets. Technical report, TR71, CSE, The Ohio State University, 2005.
    • (2005) Technical Report , vol.TR71
    • Ghoting, A.1    Parthasarathy, S.2    Otey, M.3
  • 7
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    • Finding intensional knowledge of distance-based outliers
    • E. Knorr and R. Ng. Finding intensional knowledge of distance-based outliers. In VLDB, 1999.
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    • Knorr, E.1    Ng, R.2
  • 11
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    • Efficient algorithms for mining outliers from large datasets
    • S. Ramaswamy, R. Rastogi, and K. Shim. Efficient algorithms for mining outliers from large datasets. In SIGMOD, 2000.
    • (2000) SIGMOD
    • Ramaswamy, S.1    Rastogi, R.2    Shim, K.3
  • 12
    • 0030157145 scopus 로고    scopus 로고
    • BIRCH: An efficient data clustering method for very large databases
    • T. Zhang, R. Ramakrishnan, and M. Livny. BIRCH: An efficient data clustering method for very large databases. In SIGMOD, 1996.
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    • Zhang, T.1    Ramakrishnan, R.2    Livny, M.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.