Outlier detection in arbitrarily oriented subspaces

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

*Corresponding author for this work

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

Abstract

In this paper, we propose a novel outlier detection model to find outliers that deviate from the generating mechanisms of normal instances by considering combinations of different subsets of attributes, as they occur when there are local correlations in the data set. Our model enables to search for outliers in arbitrarily oriented subspaces of the original feature space. We show how in addition to an outlier score, our model also derives an explanation of the outlierness that is useful in investigating the results. Our experiments suggest that our novel method can find different outliers than existing work and can be seen as a complement of those approaches.

Original languageEnglish
Title of host publicationProceedings of the 12th IEEE International Conference on Data Mining, ICDM 2012
PublisherIEEE
Publication dateDec 2012
Pages379-388
ISBN (Print)978-1-4673-4649-8, 978-0-7695-4905-7
DOIs
Publication statusPublished - Dec 2012
Externally publishedYes
Event12th IEEE International Conference on Data Mining - Brussels, Belgium
Duration: 10. Dec 201213. Dec 2012

Conference

Conference12th IEEE International Conference on Data Mining
CountryBelgium
CityBrussels
Period10/12/201213/12/2012

Fingerprint

Experiments

Cite this

Kriegel, H. P., Kröger, P., Schubert, E., & Zimek, A. (2012). Outlier detection in arbitrarily oriented subspaces. In Proceedings of the 12th IEEE International Conference on Data Mining, ICDM 2012 (pp. 379-388). IEEE. https://doi.org/10.1109/ICDM.2012.21
Kriegel, Hans Peter ; Kröger, Peer ; Schubert, Erich ; Zimek, Arthur. / Outlier detection in arbitrarily oriented subspaces. Proceedings of the 12th IEEE International Conference on Data Mining, ICDM 2012. IEEE, 2012. pp. 379-388
@inproceedings{c782bcbc2b75453fb961f93361eaaad2,
title = "Outlier detection in arbitrarily oriented subspaces",
abstract = "In this paper, we propose a novel outlier detection model to find outliers that deviate from the generating mechanisms of normal instances by considering combinations of different subsets of attributes, as they occur when there are local correlations in the data set. Our model enables to search for outliers in arbitrarily oriented subspaces of the original feature space. We show how in addition to an outlier score, our model also derives an explanation of the outlierness that is useful in investigating the results. Our experiments suggest that our novel method can find different outliers than existing work and can be seen as a complement of those approaches.",
author = "Kriegel, {Hans Peter} and Peer Kr{\"o}ger and Erich Schubert and Arthur Zimek",
year = "2012",
month = "12",
doi = "10.1109/ICDM.2012.21",
language = "English",
isbn = "978-1-4673-4649-8",
pages = "379--388",
booktitle = "Proceedings of the 12th IEEE International Conference on Data Mining, ICDM 2012",
publisher = "IEEE",
address = "United States",

}

Kriegel, HP, Kröger, P, Schubert, E & Zimek, A 2012, Outlier detection in arbitrarily oriented subspaces. in Proceedings of the 12th IEEE International Conference on Data Mining, ICDM 2012. IEEE, pp. 379-388, 12th IEEE International Conference on Data Mining, Brussels, Belgium, 10/12/2012. https://doi.org/10.1109/ICDM.2012.21

Outlier detection in arbitrarily oriented subspaces. / Kriegel, Hans Peter; Kröger, Peer; Schubert, Erich; Zimek, Arthur.

Proceedings of the 12th IEEE International Conference on Data Mining, ICDM 2012. IEEE, 2012. p. 379-388.

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

TY - GEN

T1 - Outlier detection in arbitrarily oriented subspaces

AU - Kriegel, Hans Peter

AU - Kröger, Peer

AU - Schubert, Erich

AU - Zimek, Arthur

PY - 2012/12

Y1 - 2012/12

N2 - In this paper, we propose a novel outlier detection model to find outliers that deviate from the generating mechanisms of normal instances by considering combinations of different subsets of attributes, as they occur when there are local correlations in the data set. Our model enables to search for outliers in arbitrarily oriented subspaces of the original feature space. We show how in addition to an outlier score, our model also derives an explanation of the outlierness that is useful in investigating the results. Our experiments suggest that our novel method can find different outliers than existing work and can be seen as a complement of those approaches.

AB - In this paper, we propose a novel outlier detection model to find outliers that deviate from the generating mechanisms of normal instances by considering combinations of different subsets of attributes, as they occur when there are local correlations in the data set. Our model enables to search for outliers in arbitrarily oriented subspaces of the original feature space. We show how in addition to an outlier score, our model also derives an explanation of the outlierness that is useful in investigating the results. Our experiments suggest that our novel method can find different outliers than existing work and can be seen as a complement of those approaches.

U2 - 10.1109/ICDM.2012.21

DO - 10.1109/ICDM.2012.21

M3 - Article in proceedings

AN - SCOPUS:84874057277

SN - 978-1-4673-4649-8

SN - 978-0-7695-4905-7

SP - 379

EP - 388

BT - Proceedings of the 12th IEEE International Conference on Data Mining, ICDM 2012

PB - IEEE

ER -

Kriegel HP, Kröger P, Schubert E, Zimek A. Outlier detection in arbitrarily oriented subspaces. In Proceedings of the 12th IEEE International Conference on Data Mining, ICDM 2012. IEEE. 2012. p. 379-388 https://doi.org/10.1109/ICDM.2012.21