Outlier detection in axis-parallel subspaces of high dimensional data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5476 LNAI, Issue , 2009, Pages 831-838

  Kriege, Hans Peter ^a   Kr̈oger, Peer ^a   Schubert, Erich ^a   Zimek, Arthur ^a  

^a UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

DATA SETS; HIGH DIMENSIONAL DATA; HIGH-DIMENSIONAL; HIGH-DIMENSIONAL FEATURE SPACE; OUTLIER DETECTION;

MINING;

CLUSTERING ALGORITHMS;

EID: 67650661596     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-01307-2_86     Document Type: Conference Paper

References (6)

1. Hawkins, D.: Identification of Outliers. Chapman and Hall, London (1980)
2. Hinneburg, A., Aggarwal, C.C., Keim, D.A.: What is the nearest neighbor in high dimensional spaces? In: Proc. VLDB (2000)
3. Kriegel, H.P., Krïoger, P., Zimek, A.: Clustering high dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering. In: ACM Transactions on Knowledge Discovery from Data (TKDD) (to appear)
4. Breunig, M.M., Kriegel, H.P., Ng, R., Sander, J.: LOF: Identifying density-based local outliers. In: Proc. SIGMOD (2000)
5. Kriegel, H.P., Schubert, M., Zimek, A.: Angle-based outlier detection in highdimensional data. In: Proc. KDD (2008)
6. Achtert, E., Kriegel, H.P., Zimek, A.: ELKI: a software system for evaluation of subspace clustering algorithms. In: Ludïascher, B., Mamoulis, N. (eds.) SSDBM 2008. LNCS, vol.5069, pp. 580-585. Springer, Heidelberg (2008)