OutRank: ranking outliers in high dimensional data

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Standard

OutRank : ranking outliers in high dimensional data. / Müller, Emmanuel; Assent, Ira; Steinhausen, Uwe; Seidl, Thomas.

IEEE 24th International Conference on Data Engineering Workshop, 2008. ICDEW 2008. 2008. p. 600-603.

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Harvard

Müller, E, Assent, I, Steinhausen, U & Seidl, T 2008, OutRank: ranking outliers in high dimensional data. in IEEE 24th International Conference on Data Engineering Workshop, 2008. ICDEW 2008. pp. 600-603. https://doi.org/10.1109/ICDEW.2008.4498387

APA

Müller, E., Assent, I., Steinhausen, U., & Seidl, T. (2008). OutRank: ranking outliers in high dimensional data. In IEEE 24th International Conference on Data Engineering Workshop, 2008. ICDEW 2008 (pp. 600-603) https://doi.org/10.1109/ICDEW.2008.4498387

CBE

Müller E, Assent I, Steinhausen U, Seidl T. 2008. OutRank: ranking outliers in high dimensional data. In IEEE 24th International Conference on Data Engineering Workshop, 2008. ICDEW 2008. pp. 600-603. https://doi.org/10.1109/ICDEW.2008.4498387

MLA

Müller, Emmanuel et al. "OutRank: ranking outliers in high dimensional data". IEEE 24th International Conference on Data Engineering Workshop, 2008. ICDEW 2008. 2008, 600-603. https://doi.org/10.1109/ICDEW.2008.4498387

Vancouver

Müller E, Assent I, Steinhausen U, Seidl T. OutRank: ranking outliers in high dimensional data. In IEEE 24th International Conference on Data Engineering Workshop, 2008. ICDEW 2008. 2008. p. 600-603 https://doi.org/10.1109/ICDEW.2008.4498387

Author

Müller, Emmanuel ; Assent, Ira ; Steinhausen, Uwe ; Seidl, Thomas. / OutRank : ranking outliers in high dimensional data. IEEE 24th International Conference on Data Engineering Workshop, 2008. ICDEW 2008. 2008. pp. 600-603

Bibtex

@inproceedings{2c58b06783104702b295fc249d7c354e,
title = "OutRank: ranking outliers in high dimensional data",
abstract = "Outlier detection is an important data mining task for consistency checks, fraud detection, etc. Binary decision making on whether or not an object is an outlier is not appropriate in many applications and moreover hard to parametrize. Thus, recently, methods for outlier ranking have been proposed. Determining the degree of deviation, they do not require setting a decision boundary between outliers and the remaining data. High dimensional and heterogeneous (continuous and categorical attributes) data, however, pose a problem for most outlier ranking algorithms. In this work, we propose our OutRank approach for ranking outliers in heterogeneous high dimensional data. We introduce a consistent model for different attribute types. Our novel scoring functions transform the analyzed structure of the data to a meaningful ranking. Promising results in preliminary experiments show the potential for successful outlier ranking in high dimensional data.",
author = "Emmanuel M{\"u}ller and Ira Assent and Uwe Steinhausen and Thomas Seidl",
year = "2008",
doi = "10.1109/ICDEW.2008.4498387",
language = "English",
isbn = "978-1-4244-2161-9",
pages = "600--603",
booktitle = "IEEE 24th International Conference on Data Engineering Workshop, 2008. ICDEW 2008",

}

RIS

TY - GEN

T1 - OutRank

T2 - ranking outliers in high dimensional data

AU - Müller, Emmanuel

AU - Assent, Ira

AU - Steinhausen, Uwe

AU - Seidl, Thomas

PY - 2008

Y1 - 2008

N2 - Outlier detection is an important data mining task for consistency checks, fraud detection, etc. Binary decision making on whether or not an object is an outlier is not appropriate in many applications and moreover hard to parametrize. Thus, recently, methods for outlier ranking have been proposed. Determining the degree of deviation, they do not require setting a decision boundary between outliers and the remaining data. High dimensional and heterogeneous (continuous and categorical attributes) data, however, pose a problem for most outlier ranking algorithms. In this work, we propose our OutRank approach for ranking outliers in heterogeneous high dimensional data. We introduce a consistent model for different attribute types. Our novel scoring functions transform the analyzed structure of the data to a meaningful ranking. Promising results in preliminary experiments show the potential for successful outlier ranking in high dimensional data.

AB - Outlier detection is an important data mining task for consistency checks, fraud detection, etc. Binary decision making on whether or not an object is an outlier is not appropriate in many applications and moreover hard to parametrize. Thus, recently, methods for outlier ranking have been proposed. Determining the degree of deviation, they do not require setting a decision boundary between outliers and the remaining data. High dimensional and heterogeneous (continuous and categorical attributes) data, however, pose a problem for most outlier ranking algorithms. In this work, we propose our OutRank approach for ranking outliers in heterogeneous high dimensional data. We introduce a consistent model for different attribute types. Our novel scoring functions transform the analyzed structure of the data to a meaningful ranking. Promising results in preliminary experiments show the potential for successful outlier ranking in high dimensional data.

U2 - 10.1109/ICDEW.2008.4498387

DO - 10.1109/ICDEW.2008.4498387

M3 - Article in proceedings

SN - 978-1-4244-2161-9

SP - 600

EP - 603

BT - IEEE 24th International Conference on Data Engineering Workshop, 2008. ICDEW 2008

ER -