Adaptive outlierness for subspace outlier ranking

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2010, Pages 1629-1632

  Müller, Emmanuel ^a   Schiffer, Matthias ^a   Seidl, Thomas ^a  

^a RWTH AACHEN UNIVERSITY   (Germany)

Author keywords

Data mining; Outliers; Ranking; Subspaces

Indexed keywords

ADAPTIVE NEIGHBORHOOD; DATA ANALYSIS; OUTLIER MINING; OUTLIERS; RANKING; RANKING APPROACH; SUBSPACE PROJECTION; SUBSPACES;

DATA MINING;

KNOWLEDGE MANAGEMENT;

EID: 78651279991     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1871437.1871690     Document Type: Conference Paper

References (9)

1. I. Assent, R. Krieger, E. Müller, and T. Seidl. DUSC: Dimensionality unbiased subspace clustering. In ICDM, pages 409-414, 2007.
2. M. Breunig, H.-P. Kriegel, R. Ng, and J. Sander. LOF: Identifying density-based local outliers. In SIGMOD, pages 93-104, 2000.
3. H.-P. Kriegel, P. Kröger, E. Schubert, and A. Zimek. Outlier detection in axis-parallel subspaces of high dimensional data. In PAKDD, pages 831-838, 2009.
4. H.-P. Kriegel, M. Schubert, and A. Zimek. Angle-based outlier detection in high-dimensional data. In KDD, pages 444-452, 2008.
5. E. Müller, I. Assent, S. Günnemann, R. Krieger, and T. Seidl. Relevant Subspace Clustering: mining the most interesting non-redundant concepts in high dimensional data. In ICDM, pages 377-386, 2009.
6. E. Müller, I. Assent, U. Steinhausen, and T. Seidl. Outrank: Ranking outliers in high dimensional data. In DBRank Workshop, pages 600-603, 2008.
7. E. Müller, S. Günnemann, I. Assent, and T. Seidl. Evaluating clustering in subspace projections of high dimensional data. PVLDB, 2(1):1270-1281, 2009.
8. E. Müller, M. Schiffer, P. Gerwert, M. Hannen, T. Jansen, and T. Seidl. SOREX: Subspace outlier ranking exploration toolkit. In ECML PKDD, pages 607-610, 2010.
9. B. Silverman. Density Estimation for Statistics and Data Analysis. Chapman and Hall, London, 1986.