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Volumn , Issue , 2008, Pages 444-452

Angle-based outlier detection in high-dimensional data

Author keywords

Angle based; High dimensional; Outlier detection

Indexed keywords

ANGLE-BASED; CURSE OF DIMENSIONALITIES; DATA MINING TASKS; DATA OBJECTS; DATA SETS; DATA SPACES; DIFFERENCE VECTORS; DIFFERENT MECHANISMS; DISTANCE-BASED; DISTANCE-BASED METHODS; EUCLIDEAN; EXPERIMENTAL EVALUATIONS; HIGH-DIMENSIONAL; HIGH-DIMENSIONAL DATUM; NEW APPROACHES; OUTLIER DETECTION; PARAMETER SELECTIONS; REAL-WORLD DATUM;

EID: 65449145220     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1401890.1401946     Document Type: Conference Paper
Times cited : (761)

References (33)
  • 1
    • 0002080138 scopus 로고    scopus 로고
    • On the surprising behavior of distance metrics in high dimensional space
    • C. C. Aggarwal, A. Hlnneburg, and D. Keim. On the surprising behavior of distance metrics in high dimensional space. In Proc. ICDT, 2001.
    • (2001) Proc. ICDT
    • Aggarwal, C.C.1    Hlnneburg, A.2    Keim, D.3
  • 2
    • 0034832620 scopus 로고    scopus 로고
    • Outlier detection for high dimensional data
    • C. C. Aggarwal and P. S. Yu. Outlier detection for high dimensional data. In Proc. SIGMOD, 2001.
    • (2001) Proc. SIGMOD
    • Aggarwal, C.C.1    Yu, P.S.2
  • 3
    • 79957798213 scopus 로고    scopus 로고
    • Fast outlier detection in high dimensional spaces
    • F. Angiulli and C. Pizzuti. Fast outlier detection in high dimensional spaces. In Proc. PKDD, 2002.
    • (2002) Proc. PKDD
    • Angiulli, F.1    Pizzuti, C.2
  • 4
    • 85039571873 scopus 로고    scopus 로고
    • A linear method for deviation detection in large databases
    • A. Arning, R. Agrawal, and P. Raghavan. A linear method for deviation detection in large databases. In Proc. KDD, 1996.
    • (1996) Proc. KDD
    • Arning, A.1    Agrawal, R.2    Raghavan, P.3
  • 6
    • 77952380096 scopus 로고    scopus 로고
    • Mining distance-based outliers in near linear time with randomization and a simple pruning rule
    • S. Bay and M. Schwabacher. Mining distance-based outliers in near linear time with randomization and a simple pruning rule. In Proc. KDD, 2003.
    • (2003) Proc. KDD
    • Bay, S.1    Schwabacher, M.2
  • 9
    • 68749117876 scopus 로고    scopus 로고
    • A nonparametric outlier detection for efficiently discovering top-N outliers from engineering data
    • H. Fan, O. R. Zaïane, A. Foss, and J. Wu. A nonparametric outlier detection for efficiently discovering top-N outliers from engineering data. In Proc. PAKDD, 2006.
    • (2006) Proc. PAKDD
    • Fan, H.1    Zaïane, O.R.2    Foss, A.3    Wu, J.4
  • 10
    • 84932617705 scopus 로고    scopus 로고
    • Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
    • L. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. In IEEE. CVPR 2004, Workshop on Generative-Model Based Vision, 2004.
    • (2004) IEEE. CVPR 2004, Workshop on Generative-Model Based Vision
    • Fei-Fei, L.1    Fergus, R.2    Perona, P.3
  • 11
    • 4444231365 scopus 로고    scopus 로고
    • A survey of kernels for structured data
    • T. G. Gärtner. A survey of kernels for structured data. SIGKDD Explor. Newsl, 5(1):49 58, 2003.
    • (2003) SIGKDD Explor. Newsl , vol.5 , Issue.1 , pp. 49-58
    • Gärtner, T.G.1
  • 13
    • 1542292055 scopus 로고    scopus 로고
    • What is the nearest neighbor in high dimensional spaces?
    • A. Hinneburg, C. C. Aggarwal, and D. A. Keim. What is the nearest neighbor in high dimensional spaces? In Proc. VLDB, 2000.
    • (2000) Proc. VLDB
    • Hinneburg, A.1    Aggarwal, C.C.2    Keim, D.A.3
  • 14
    • 0035788909 scopus 로고    scopus 로고
    • Mining top-n local outliers in large databases
    • W. Jin, A. Tung, and J. Han. Mining top-n local outliers in large databases. In Proc. KDD, 2001.
    • (2001) Proc. KDD
    • Jin, W.1    Tung, A.2    Han, J.3
  • 15
    • 40749105193 scopus 로고    scopus 로고
    • Ranking outliers using symmetric neighborhood relationship
    • W. Jin, A. K. H. Tung, J. Han, and W. Wang. Ranking outliers using symmetric neighborhood relationship. In Proc. PAKDD, 2006.
    • (2006) Proc. PAKDD
    • Jin, W.1    Tung, A.K.H.2    Han, J.3    Wang, W.4
  • 16
    • 84977797978 scopus 로고    scopus 로고
    • Fast computation of 2-dimensional depth contours
    • T. Johnson, I. Kwok, and R. Ng. Fast computation of 2-dimensional depth contours. In Proc. KDD, 1998.
    • (1998) Proc. KDD
    • Johnson, T.1    Kwok, I.2    Ng, R.3
  • 17
    • 38049097751 scopus 로고    scopus 로고
    • A unified approach for mining outliers
    • E. M. Knorr and R. T. Ng. A unified approach for mining outliers. In Proc. GASCON, 1997.
    • (1997) Proc. GASCON
    • Knorr, E.M.1    Ng, R.T.2
  • 18
    • 0002948319 scopus 로고    scopus 로고
    • Algorithms for mining distance-based outliers in large dataseis
    • E. M. Knorr and R. T. Ng. Algorithms for mining distance-based outliers in large dataseis. In Proc. VLDB, 1998.
    • (1998) Proc. VLDB
    • Knorr, E.M.1    Ng, R.T.2
  • 20
    • 0141613770 scopus 로고    scopus 로고
    • Efficient biased sampling for approximate clustering and outlier detection in large datasets
    • G. Kollios, D. Gunopulos, N. Koudas, and S. Berchthold. Efficient biased sampling for approximate clustering and outlier detection in large datasets. IEEE TKDE, 15(5):1170 1187, 2003.
    • (2003) IEEE TKDE , vol.15 , Issue.5 , pp. 1170-1187
    • Kollios, G.1    Gunopulos, D.2    Koudas, N.3    Berchthold, S.4
  • 23
    • 84878080825 scopus 로고    scopus 로고
    • An efficient reference-based approach to outlier detection in large datasets
    • Y. Pei, O. Zaïane, and Y. Gao. An efficient reference-based approach to outlier detection in large datasets. In Proc. ICDM, 2006.
    • (2006) Proc. ICDM
    • Pei, Y.1    Zaïane, O.2    Gao, Y.3
  • 24
    • 0039845384 scopus 로고    scopus 로고
    • Efficient algorithms for mining outliers from large data sets
    • S. Ramaswamy, R. Rastogi, and K. Shim. Efficient algorithms for mining outliers from large data sets. In Proc. SIGMOD, 2000.
    • (2000) Proc. SIGMOD
    • Ramaswamy, S.1    Rastogi, R.2    Shim, K.3
  • 25
    • 0032680362 scopus 로고    scopus 로고
    • A fast algorithm for the minimum covariance determinant estimator
    • P. Rousseeuw and K. Van Driessen. A fast algorithm for the minimum covariance determinant estimator. Technometrica, 41:212-223, 1999.
    • (1999) Technometrica , vol.41 , pp. 212-223
    • Rousseeuw, P.1    Van Driessen, K.2
  • 27
    • 0000994889 scopus 로고    scopus 로고
    • Discovery-driven exploration of OLAP data cubes
    • S. Sarawagi, R. Agrawal, and N. Megiddo. Discovery-driven exploration of OLAP data cubes. In Proc. EDBT, 1998.
    • (1998) Proc. EDBT
    • Sarawagi, S.1    Agrawal, R.2    Megiddo, N.3
  • 28
    • 19544393356 scopus 로고    scopus 로고
    • On local spatial outliers
    • P. Sun and S. Chawla. On local spatial outliers. In Proc. ICDM, 2004.
    • (2004) Proc. ICDM
    • Sun, P.1    Chawla, S.2
  • 29
    • 1542326289 scopus 로고    scopus 로고
    • Enhancing effectiveness of outlier detections for low density patterns
    • J. Tang, Z. Chen, A. W.-C. Fu, and D. W. Cheung. Enhancing effectiveness of outlier detections for low density patterns. In Proc. PAKDD, 2002.
    • (2002) Proc. PAKDD
    • Tang, J.1    Chen, Z.2    Fu, A.W.-C.3    Cheung, D.W.4
  • 31
    • 77955348025 scopus 로고    scopus 로고
    • Online unsupervised outlier detection using finite mixtures with discounting learning algorithms
    • G. Williams, K. Yamanishi, and J. Takeuchi. Online unsupervised outlier detection using finite mixtures with discounting learning algorithms. In Proc. KDD, 2000.
    • (2000) Proc. KDD
    • Williams, G.1    Yamanishi, K.2    Takeuchi, J.3
  • 32
    • 77955382993 scopus 로고    scopus 로고
    • Discovering outlier filtering rules from unlabeled data: Combining a supervised learner with an unsupervised learner
    • K. Yamanishi and J. Takeuchi. Discovering outlier filtering rules from unlabeled data: combining a supervised learner with an unsupervised learner. In Proc. KDD, 2001.
    • (2001) Proc. KDD
    • Yamanishi, K.1    Takeuchi, J.2
  • 33
    • 34548588734 scopus 로고    scopus 로고
    • Example-based robust outlier detection in high dimensional datasets
    • C. Zhu, H. Kitagawa, and C. Faloutsos. Example-based robust outlier detection in high dimensional datasets. In Proc. ICDM, 2005.
    • (2005) Proc. ICDM
    • Zhu, C.1    Kitagawa, H.2    Faloutsos, C.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.