Outlier detection in axis-parallel subspaces of high dimensional data

Hans Peter Kriegel, Peer Kr̈oger, Erich Schubert, Arthur Zimek*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

We propose an original outlier detection schema that detects outliers in varying subspaces of a high dimensional feature space. In particular, for each object in the data set, we explore the axis-parallelsubspace spanned by its neighbors and determine how much the object deviates from the neighbors in this subspace. In our experiments, we show that our novel subspace outlier detection is superior to existing fulldimensional approaches and scales well to high dimensional databases.

Original languageEnglish
Title of host publication13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
EditorsT. Theeramunkong, B. Kijsirikul, N. Cercone, TB. Ho
PublisherSpringer
Publication date23. Jul 2009
Pages831-838
ISBN (Print)978-3-642-01306-5
ISBN (Electronic)978-3-642-01307-2
DOIs
Publication statusPublished - 23. Jul 2009
Externally publishedYes
Event13th Pacific-Asia Conference on Knowledge Discovery and Data Mining - Bangkok, Thailand
Duration: 27. Apr 200930. Apr 2009

Conference

Conference13th Pacific-Asia Conference on Knowledge Discovery and Data Mining
CountryThailand
CityBangkok
Period27/04/200930/04/2009
SponsorNational Electronics and Computer Technology Center (NECTEC), Thailand Convention and Exhibition Bureau (TCEB), Air Force Office of Scientific Research,, Asian Office of Aerospace Research and Development (AFOSR/AOARD)
SeriesLecture Notes in Computer Science
Volume5476
ISSN0302-9743

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Experiments

Cite this

Kriegel, H. P., Kr̈oger, P., Schubert, E., & Zimek, A. (2009). Outlier detection in axis-parallel subspaces of high dimensional data. In T. Theeramunkong, B. Kijsirikul, N. Cercone, & TB. Ho (Eds.), 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009 (pp. 831-838). Springer. Lecture Notes in Computer Science, Vol.. 5476 https://doi.org/10.1007/978-3-642-01307-2_86
Kriegel, Hans Peter ; Kr̈oger, Peer ; Schubert, Erich ; Zimek, Arthur. / Outlier detection in axis-parallel subspaces of high dimensional data. 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009. editor / T. Theeramunkong ; B. Kijsirikul ; N. Cercone ; TB. Ho. Springer, 2009. pp. 831-838 (Lecture Notes in Computer Science, Vol. 5476).
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Kriegel, HP, Kr̈oger, P, Schubert, E & Zimek, A 2009, Outlier detection in axis-parallel subspaces of high dimensional data. in T Theeramunkong, B Kijsirikul, N Cercone & TB Ho (eds), 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009. Springer, Lecture Notes in Computer Science, vol. 5476, pp. 831-838, 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Bangkok, Thailand, 27/04/2009. https://doi.org/10.1007/978-3-642-01307-2_86

Outlier detection in axis-parallel subspaces of high dimensional data. / Kriegel, Hans Peter; Kr̈oger, Peer; Schubert, Erich; Zimek, Arthur.

13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009. ed. / T. Theeramunkong; B. Kijsirikul; N. Cercone; TB. Ho. Springer, 2009. p. 831-838 (Lecture Notes in Computer Science, Vol. 5476).

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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AB - We propose an original outlier detection schema that detects outliers in varying subspaces of a high dimensional feature space. In particular, for each object in the data set, we explore the axis-parallelsubspace spanned by its neighbors and determine how much the object deviates from the neighbors in this subspace. In our experiments, we show that our novel subspace outlier detection is superior to existing fulldimensional approaches and scales well to high dimensional databases.

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Kriegel HP, Kr̈oger P, Schubert E, Zimek A. Outlier detection in axis-parallel subspaces of high dimensional data. In Theeramunkong T, Kijsirikul B, Cercone N, Ho TB, editors, 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009. Springer. 2009. p. 831-838. (Lecture Notes in Computer Science, Vol. 5476). https://doi.org/10.1007/978-3-642-01307-2_86