Generalized outlier detection with flexible kernel density estimates

Erich Schubert, Arthur Zimek, Hans Peter Kriegel

Publikation: Bidrag til bog/antologi/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

Resumé

We analyse the interplay of density estimation and outlier detection in density-based outlier detection. By clear and principled decoupling of both steps, we formulate a generalization of density-based outlier detection methods based on kernel density estimation. Embedded in a broader framework for outlier detection, the resulting method can be easily adapted to detect novel types of outliers: while common outlier detection methods are designed for detecting objects in sparse areas of the data set, our method can be modified to also detect unusual local concentrations or trends in the data set if desired. It allows for the integration of domain knowledge and specific requirements. We demonstrate the flexible applicability and scalability of the method on large real world data sets.

OriginalsprogEngelsk
TitelProceedings of the 2014 SIAM International Conference on Data Mining
RedaktørerMohammed Zaki, Zoran Obradovic, Pang Ning-Tan, Arindam Banerjee, Chandrika Kamath, Srinivasan Parthasarathy
ForlagSociety for Industrial and Applied Mathematics Publications
Publikationsdato2014
Sider542-550
ISBN (Elektronisk)978-1-61197-344-0
DOI
StatusUdgivet - 2014
Udgivet eksterntJa
Begivenhed14th SIAM International Conference on Data Mining - Philadelphia, USA
Varighed: 24. apr. 201426. apr. 2014

Konference

Konference14th SIAM International Conference on Data Mining
LandUSA
ByPhiladelphia
Periode24/04/201426/04/2014
SponsorAmerican Statistical Association

Fingeraftryk

Scalability

Citer dette

Schubert, E., Zimek, A., & Kriegel, H. P. (2014). Generalized outlier detection with flexible kernel density estimates. I M. Zaki, Z. Obradovic, P. Ning-Tan, A. Banerjee, C. Kamath, & S. Parthasarathy (red.), Proceedings of the 2014 SIAM International Conference on Data Mining (s. 542-550). Society for Industrial and Applied Mathematics Publications. https://doi.org/10.1137/1.9781611973440.63
Schubert, Erich ; Zimek, Arthur ; Kriegel, Hans Peter. / Generalized outlier detection with flexible kernel density estimates. Proceedings of the 2014 SIAM International Conference on Data Mining. red. / Mohammed Zaki ; Zoran Obradovic ; Pang Ning-Tan ; Arindam Banerjee ; Chandrika Kamath ; Srinivasan Parthasarathy. Society for Industrial and Applied Mathematics Publications, 2014. s. 542-550
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title = "Generalized outlier detection with flexible kernel density estimates",
abstract = "We analyse the interplay of density estimation and outlier detection in density-based outlier detection. By clear and principled decoupling of both steps, we formulate a generalization of density-based outlier detection methods based on kernel density estimation. Embedded in a broader framework for outlier detection, the resulting method can be easily adapted to detect novel types of outliers: while common outlier detection methods are designed for detecting objects in sparse areas of the data set, our method can be modified to also detect unusual local concentrations or trends in the data set if desired. It allows for the integration of domain knowledge and specific requirements. We demonstrate the flexible applicability and scalability of the method on large real world data sets.",
author = "Erich Schubert and Arthur Zimek and Kriegel, {Hans Peter}",
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Schubert, E, Zimek, A & Kriegel, HP 2014, Generalized outlier detection with flexible kernel density estimates. i M Zaki, Z Obradovic, P Ning-Tan, A Banerjee, C Kamath & S Parthasarathy (red), Proceedings of the 2014 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics Publications, s. 542-550, 14th SIAM International Conference on Data Mining, Philadelphia, USA, 24/04/2014. https://doi.org/10.1137/1.9781611973440.63

Generalized outlier detection with flexible kernel density estimates. / Schubert, Erich; Zimek, Arthur; Kriegel, Hans Peter.

Proceedings of the 2014 SIAM International Conference on Data Mining. red. / Mohammed Zaki; Zoran Obradovic; Pang Ning-Tan; Arindam Banerjee; Chandrika Kamath; Srinivasan Parthasarathy. Society for Industrial and Applied Mathematics Publications, 2014. s. 542-550.

Publikation: Bidrag til bog/antologi/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

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Schubert E, Zimek A, Kriegel HP. Generalized outlier detection with flexible kernel density estimates. I Zaki M, Obradovic Z, Ning-Tan P, Banerjee A, Kamath C, Parthasarathy S, red., Proceedings of the 2014 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics Publications. 2014. s. 542-550 https://doi.org/10.1137/1.9781611973440.63